{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-2-89cf4471ee6e>:14: FutureWarning: The pandas.datetime class is deprecated and will be removed from pandas in a future version. Import from datetime module instead.\n",
      "  dateparse = lambda dates: pd.datetime.strptime(dates, '%Y%m%d')\n",
      "<ipython-input-2-89cf4471ee6e>:25: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  user_balance = user_balance.groupby(['report_date'])['share_amt', 'total_purchase_amt'].sum()\n",
      "<ipython-input-2-89cf4471ee6e>:145: FutureWarning: The pandas.datetime class is deprecated and will be removed from pandas in a future version. Import from datetime module instead.\n",
      "  dateparse = lambda dates: pd.datetime.strptime(dates, '%Y-%m-%d')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 1 fit loss: 0.3962328 valid loss: 0.19282429\n",
      "epoch: 2 fit loss: 0.1333817 valid loss: 0.20796962\n",
      "epoch: 3 fit loss: 0.074010134 valid loss: 0.06888441\n",
      "epoch: 4 fit loss: 0.05132934 valid loss: 0.038582794\n",
      "epoch: 5 fit loss: 0.04274188 valid loss: 0.024357487\n",
      "epoch: 6 fit loss: 0.04448758 valid loss: 0.01948597\n",
      "epoch: 7 fit loss: 0.041806232 valid loss: 0.01870454\n",
      "epoch: 8 fit loss: 0.04095451 valid loss: 0.019283762\n",
      "epoch: 9 fit loss: 0.039561115 valid loss: 0.017891858\n",
      "epoch: 10 fit loss: 0.039102945 valid loss: 0.017439192\n",
      "epoch: 11 fit loss: 0.038790837 valid loss: 0.017673763\n",
      "epoch: 12 fit loss: 0.038524408 valid loss: 0.01720101\n",
      "epoch: 13 fit loss: 0.038410705 valid loss: 0.016564343\n",
      "epoch: 14 fit loss: 0.038328562 valid loss: 0.016875735\n",
      "epoch: 15 fit loss: 0.037698068 valid loss: 0.01628435\n",
      "epoch: 16 fit loss: 0.03747333 valid loss: 0.015818138\n",
      "epoch: 17 fit loss: 0.036960438 valid loss: 0.0161107\n",
      "epoch: 18 fit loss: 0.036911063 valid loss: 0.01594544\n",
      "epoch: 19 fit loss: 0.037343264 valid loss: 0.016187996\n",
      "epoch: 20 fit loss: 0.03702617 valid loss: 0.015948026\n",
      "epoch: 21 fit loss: 0.03639923 valid loss: 0.016294219\n",
      "epoch: 22 fit loss: 0.036535252 valid loss: 0.016137311\n",
      "epoch: 23 fit loss: 0.03618494 valid loss: 0.016128937\n",
      "epoch: 24 fit loss: 0.035893094 valid loss: 0.016238615\n",
      "epoch: 25 fit loss: 0.03628857 valid loss: 0.016365292\n",
      "epoch: 26 fit loss: 0.03550414 valid loss: 0.016545242\n",
      "epoch: 27 fit loss: 0.03551991 valid loss: 0.016623273\n",
      "epoch: 28 fit loss: 0.0343365 valid loss: 0.0168764\n",
      "epoch: 29 fit loss: 0.034503587 valid loss: 0.016817037\n",
      "epoch: 30 fit loss: 0.033562932 valid loss: 0.016550595\n",
      "epoch: 31 fit loss: 0.035138514 valid loss: 0.016395139\n",
      "epoch: 32 fit loss: 0.033704598 valid loss: 0.017031083\n",
      "epoch: 33 fit loss: 0.033264548 valid loss: 0.016364653\n",
      "epoch: 34 fit loss: 0.03325166 valid loss: 0.016393432\n",
      "epoch: 35 fit loss: 0.03126696 valid loss: 0.016927779\n",
      "epoch: 36 fit loss: 0.030317921 valid loss: 0.017255798\n",
      "epoch: 37 fit loss: 0.028481117 valid loss: 0.016310725\n",
      "epoch: 38 fit loss: 0.027574863 valid loss: 0.017671755\n",
      "epoch: 39 fit loss: 0.0266991 valid loss: 0.017227361\n",
      "epoch: 40 fit loss: 0.026044592 valid loss: 0.016759131\n",
      "epoch: 41 fit loss: 0.025610544 valid loss: 0.0168726\n",
      "epoch: 42 fit loss: 0.025577322 valid loss: 0.015425913\n",
      "epoch: 43 fit loss: 0.023251165 valid loss: 0.01591415\n",
      "epoch: 44 fit loss: 0.023062758 valid loss: 0.015798237\n",
      "epoch: 45 fit loss: 0.0225209 valid loss: 0.014421515\n",
      "epoch: 46 fit loss: 0.022337727 valid loss: 0.015205816\n",
      "epoch: 47 fit loss: 0.021713406 valid loss: 0.015094097\n",
      "epoch: 48 fit loss: 0.02100173 valid loss: 0.014916624\n",
      "epoch: 49 fit loss: 0.021019626 valid loss: 0.014705734\n",
      "epoch: 50 fit loss: 0.020932956 valid loss: 0.014114323\n",
      "epoch: 51 fit loss: 0.020196535 valid loss: 0.014498337\n",
      "epoch: 52 fit loss: 0.020174421 valid loss: 0.014387159\n",
      "epoch: 53 fit loss: 0.019780822 valid loss: 0.014085377\n",
      "epoch: 54 fit loss: 0.01941444 valid loss: 0.014464349\n",
      "epoch: 55 fit loss: 0.01958611 valid loss: 0.014493057\n",
      "epoch: 56 fit loss: 0.019835591 valid loss: 0.014348773\n",
      "epoch: 57 fit loss: 0.019500816 valid loss: 0.014190366\n",
      "epoch: 58 fit loss: 0.019167835 valid loss: 0.013952096\n",
      "epoch: 59 fit loss: 0.019844005 valid loss: 0.013685882\n",
      "epoch: 60 fit loss: 0.0197903 valid loss: 0.013798489\n",
      "epoch: 61 fit loss: 0.020215934 valid loss: 0.01436971\n",
      "epoch: 62 fit loss: 0.021335274 valid loss: 0.015038459\n",
      "epoch: 63 fit loss: 0.019048888 valid loss: 0.014918588\n",
      "epoch: 64 fit loss: 0.0188437 valid loss: 0.014067839\n",
      "epoch: 65 fit loss: 0.020564385 valid loss: 0.014216702\n",
      "epoch: 66 fit loss: 0.019966573 valid loss: 0.01479293\n",
      "epoch: 67 fit loss: 0.018562581 valid loss: 0.014956962\n",
      "epoch: 68 fit loss: 0.019463792 valid loss: 0.014777192\n",
      "epoch: 69 fit loss: 0.018960353 valid loss: 0.015125429\n",
      "epoch: 70 fit loss: 0.017966805 valid loss: 0.014300885\n",
      "epoch: 71 fit loss: 0.018404199 valid loss: 0.015676335\n",
      "epoch: 72 fit loss: 0.017895292 valid loss: 0.01646699\n",
      "epoch: 73 fit loss: 0.017874004 valid loss: 0.016220607\n",
      "epoch: 74 fit loss: 0.017466182 valid loss: 0.016837537\n",
      "epoch: 75 fit loss: 0.01757803 valid loss: 0.014627478\n",
      "epoch: 76 fit loss: 0.016967159 valid loss: 0.01520153\n",
      "epoch: 77 fit loss: 0.017201858 valid loss: 0.016767833\n",
      "epoch: 78 fit loss: 0.016264029 valid loss: 0.016017498\n",
      "epoch: 79 fit loss: 0.016417118 valid loss: 0.016682371\n",
      "epoch: 80 fit loss: 0.01710658 valid loss: 0.017189011\n",
      "epoch: 81 fit loss: 0.014961721 valid loss: 0.015924605\n",
      "epoch: 82 fit loss: 0.01644413 valid loss: 0.017315257\n",
      "epoch: 83 fit loss: 0.014719673 valid loss: 0.017868\n",
      "epoch: 84 fit loss: 0.014671106 valid loss: 0.01687575\n",
      "epoch: 85 fit loss: 0.015350805 valid loss: 0.018695002\n",
      "epoch: 86 fit loss: 0.013782995 valid loss: 0.018117502\n",
      "epoch: 87 fit loss: 0.014523393 valid loss: 0.016962644\n",
      "epoch: 88 fit loss: 0.014049838 valid loss: 0.017970536\n",
      "epoch: 89 fit loss: 0.0134325335 valid loss: 0.016557673\n",
      "epoch: 90 fit loss: 0.013809045 valid loss: 0.018013027\n",
      "epoch: 91 fit loss: 0.014235071 valid loss: 0.019452762\n",
      "epoch: 92 fit loss: 0.013971942 valid loss: 0.017252496\n",
      "epoch: 93 fit loss: 0.012732141 valid loss: 0.01595586\n",
      "epoch: 94 fit loss: 0.012864506 valid loss: 0.017238954\n",
      "epoch: 95 fit loss: 0.012024246 valid loss: 0.016233161\n",
      "epoch: 96 fit loss: 0.01219741 valid loss: 0.016933264\n",
      "epoch: 97 fit loss: 0.012520619 valid loss: 0.016521845\n",
      "epoch: 98 fit loss: 0.012594591 valid loss: 0.017521063\n",
      "epoch: 99 fit loss: 0.011121353 valid loss: 0.015924254\n",
      "epoch: 100 fit loss: 0.0116805835 valid loss: 0.015602963\n",
      "epoch: 101 fit loss: 0.011494057 valid loss: 0.016547212\n",
      "epoch: 102 fit loss: 0.011238992 valid loss: 0.017051456\n",
      "epoch: 103 fit loss: 0.011529331 valid loss: 0.016675487\n",
      "epoch: 104 fit loss: 0.011146056 valid loss: 0.018063797\n",
      "epoch: 105 fit loss: 0.011050672 valid loss: 0.016763236\n",
      "epoch: 106 fit loss: 0.010041225 valid loss: 0.016636329\n",
      "epoch: 107 fit loss: 0.009790593 valid loss: 0.017141473\n",
      "epoch: 108 fit loss: 0.010193484 valid loss: 0.016695797\n",
      "epoch: 109 fit loss: 0.010691562 valid loss: 0.016336974\n",
      "epoch: 110 fit loss: 0.009919241 valid loss: 0.017376674\n",
      "epoch: 111 fit loss: 0.009757737 valid loss: 0.01766719\n",
      "epoch: 112 fit loss: 0.009067934 valid loss: 0.015284468\n",
      "best epoch:  112\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABCAAAAK7CAYAAAAjnf7fAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzde5ycZX3//9e1M7Pn3WTJbpLNgRyBnEgCRaBQRMUKYoCqCKlWDZZaqqhoqdD6tUWl/SH1q9SWg9YCHugXMAgiUvBRQBFrxURICJhoEhJy3nP2ODun6/fHNffu7GZ2d2Z3dufemffz8chjZu65Z+aazSZz3+/5XJ/LWGsREREREREREZlMJfkegIiIiIiIiIgUPgUQIiIiIiIiIjLpFECIiIiIiIiIyKRTACEiIiIiIiIik04BhIiIiIiIiIhMOgUQIiIiIiIiIjLpFECIFCFjzD5jzNvzPQ4RERGRbBlj7jHGfD7f4xCR7AXzPQARERERESkexph9wLXW2v8ez+OttdfldkQiMlVUASEiIiIiIr5gjNEXpCIFTAGESBEzxpQZY+4wxhxO/rnDGFOWvK/eGPOEMabDGNNmjPm5MaYked9NxphDxpguY8wuY8xF+X0nIiIiMh0YY74LnAz8yBjTbYz5rDHGGmP+3BjzBvBscr/vG2OOGmOOG2OeN8asTnmO+40xtyavv8UYc9AY89fGmCZjzBFjzDV5eXMiMiYFECLF7XPAucB6YB1wNvB/kvf9NXAQaADmAH8HWGPMacD1wJustTXAxcC+qR22iIiITEfW2g8CbwCXWWurgYeTd10IrMQdVwD8F3AKMBv4DfDAKE87F5gBzAf+HLjTGFOX+9GLyEQpgBApbh8AvmitbbLWNgNfAD6YvC8KNAKLrLVRa+3PrbUWiANlwCpjTMhau89auycvoxcRmWTGmHuT36ruyGDfk40xzxljXjLGbDfGXDoVYxQpELdYa3ustX0A1tp7rbVd1tp+4BZgnTFmxgiPjeKOZ6LW2ieBbuC0KRm1iGRFAYRIcZsH7E+5vT+5DeCfgd3AT4wxe40xNwNYa3cDN+AOBpqMMQ8aY+YhIlKY7gcuyXDf/wM8bK09A9gI3DVZgxIpQAe8K8aYgDHmNmPMHmNMJ4OVlvUjPLbVWhtLud0LVE/OMEVkIhRAiBS3w8CilNsnJ7eR/Nbhr621S4HLgM94vR6stf9prf2j5GMt8OWpHbaIyNSw1j4PtKVuM8YsM8Y8ZYzZmuyPs8LbHahNXp9B8v9TETmBHWPb+4ErgLfj/i0tTm43kzssEZls6jIrUtz+H/B/jDG/xn3w/z3wPQBjzAZgJ7AH6MRNvYgne0DMB34BhIE+FGaKSHH5JnCdtfb3xphzcJUOb8NVhv3EGPMJoAp38iQiJzoGLB3l/hqgH2gFKoF/mopBicjk00mDSHG7FdgCbAdewTV5ujV53ynAf+PmUf4SuMta+1Nc/4fbgBbgKK451N9N6ahFRPLEGFMNnAd83xjzMvANXL8cgD8F7rfWLgAuBb7rrR4kIkP8f7gvQDqAK9Pc/x3ctNBDwGvA/07h2ERkEhnXU05ERERE0jHGLAaesNauMcbUArustY1p9nsVuMRaeyB5ey9wrrW2aSrHKyIi4ldK5UVEREQyZK3tBF43xrwPwDjrkne/AVyU3L4SKAea8zJQERERH1IFhIiIiMgIjDH/D3gLrvv+MeAfgGeBu3FTL0LAg9baLxpjVgH/juu+b4HPWmt/ko9xi4iI+JECCBERERERERGZdJqCISIiIiIiIiKTzpfLcNbX19vFixfnexgiIiK+snXr1hZrbUO+x1EMdCwiIiKS3kSOR3wZQCxevJgtW7bkexgiIiK+YozZn+8xFAsdi4iIiKQ3keMRTcEQERERERERkUmnAEJEREREREREJp0CCBERERERERGZdL7sASEiItNPNBrl4MGDhMPhfA9l2isvL2fBggWEQqF8D0VERGTa0zHK+EzG8YgCCBERyYmDBw9SU1PD4sWLMcbkezjTlrWW1tZWDh48yJIlS/I9HBERkWlPxyjZm6zjEU3BEBGRnAiHw8yaNUsf7BNkjGHWrFn6lkZERCRHdIySvck6HskogDDGXGKM2WWM2W2MuXmU/d5kjIkbY67M9rEiIjL96YM9N/RzFBERyS19tmZvMn5mYwYQxpgAcCfwTmAV8KfGmFUj7Pdl4OlsHysiIiIiIiIihS2TCoizgd3W2r3W2gjwIHBFmv0+ATwCNI3jsSIiIiIiIiICVFdX53sIkyKTAGI+cCDl9sHktgHGmPnAu4F7sn1synN81BizxRizpbm5OYNhiYiIDPX1r3+dlStXUldXx2233QbAY489xmuvvTbq4zZt2sTmzZunYogiIiIiRSuTACLdxA877PYdwE3W2vg4Hus2WvtNa+1Z1tqzGhoaMhiWiIjIUHfddRdPPvkk7e3t3HyzazuUSQAhIiIiMpluuukm7rrrroHbt9xyC1/4whe46KKLOPPMMzn99NP54Q9/eMLjfvrTn7Jhw4aB29dffz33338/AFu3buXCCy/kD/7gD7j44os5cuTIpL+PicpkGc6DwMKU2wuAw8P2OQt4MNmkoh641BgTy/Cxk+qhh+D22+HZZ2HGjKl8ZRGR4nXDDfDyy7l9zvXr4Y47Rr7/uuuuY+/evVx++eV85CMfYc+ePbz//e/n8ccf52c/+xm33norjzzyCMuWLRv1dZ555hluvPFGYrEYb3rTm7j77rspKyvj5ptv5vHHHycYDPKOd7yDr3zlK3z/+9/nC1/4AoFAgBkzZvD888/n9k1L0fvc5+D88+HSS/M9EhGRwpCPYxSAjRs3csMNN/Cxj30MgIcffpinnnqKT3/609TW1tLS0sK5557L5ZdfnlHzx2g0yic+8Ql++MMf0tDQwEMPPcTnPvc57r333ly8pUmTSQDxa+AUY8wS4BCwEXh/6g7W2oGFQY0x9wNPWGsfM8YEx3rsZGtthd/8BsJhBRAiIoXsnnvu4amnnuK5557jiSeeAOC8887j8ssvZ8OGDVx55ZVjPINbpmvTpk0888wznHrqqXzoQx/i7rvv5kMf+hCPPvooO3fuxBhDR0cHAF/84hd5+umnmT9//sA2kVz6t3+DlhYFECIi090ZZ5xBU1MThw8fprm5mbq6OhobG/n0pz/N888/T0lJCYcOHeLYsWPMnTt3zOfbtWsXO3bs4I//+I8BiMfjNDY2TvbbmLAxAwhrbcwYcz1udYsAcK+19lVjzHXJ+4f3fRjzsbkZemZCIXcZjU7lq4qIFLexvgXwq127drFkyRJOPfVUAD784Q9z5513cv3111NeXs61117Lu971roFSyPPPP59NmzZx1VVX8Z73vCefQ5cC1dsLkUi+RyEiUjjyeYxy5ZVXsnnzZo4ePcrGjRt54IEHaG5uZuvWrYRCIRYvXkw4HB7ymGAwSCKRGLjt3W+tZfXq1fzyl7+c0vcwUZn0gMBa+6S19lRr7TJr7T8mt92TLnyw1m6y1m4e7bFTSQGEiIhkytq0bYoIBoO8+OKLvPe97+Wxxx7jkksuAVzVxa233sqBAwdYv349ra2tUzlcKXCRCMRi0N+f75GIiEgubNy4kQcffJDNmzdz5ZVXcvz4cWbPnk0oFOK5555j//79Jzxm0aJFvPbaa/T393P8+HGeeeYZAE477TSam5sHAohoNMqrr07pd/3jkskUjGlNAYSISHGrqamhq6sro31XrFjBvn372L17N8uXL+e73/0uF154Id3d3fT29nLppZdy7rnnsnz5cgD27NnDOeecwznnnMOPfvQjDhw4wKxZsybz7UgR6e11l6qAEBEpDKtXr6arq4v58+fT2NjIBz7wAS677DLOOuss1q9fz4oVK054zMKFC7nqqqtYu3Ytp5xyCmeccQYApaWlbN68mU9+8pMcP36cWCzGDTfcwOrVq6f6bWWl4AOI0lJ3qQ9vEZHitHHjRv7iL/6Cr3/962zevHnUJpTl5eXcd999vO997xtoQnndddfR1tbGFVdcQTgcxlrL1772NQD+5m/+ht///vdYa7noootYt27dVL0tKQI9Pe5SxzAiIoXjlVdeGbheX18/4hSK7u7ugeu33347t99++wn7rF+/fto1wC74AEIVECIixWPfvn0AbNq0iU2bNgGuT8NYy3B6y1kBXHTRRbz00ktD7m9sbOTFF1884XE/+MEPJjRekdGoAkJERApNRj0gpjMFECIiIjIdeRUQ6gEhIiKFQhUQIiJSND7+8Y/zi1/8Ysi2T33qU1xzzTV5GpHIyDQFQ0RECk3BBxDqASEiIp4777wz30MQyZimYIiISKHRFAwRERERH1IFhIiIFBoFECIiIiI+pAoIEREpNAogRERERHxITShFRKTQFHwA4fWAUAAhIiIi04kqIERECsfXv/51Vq5cSV1dHbfddhsAjz322JhLhWdr06ZNbN68OafPmUsFH0B4FRD68BYRkVTV1dUAHD58mCuvvDLtPm95y1vYsmXLiM+xePFiWlpaJmV8IuoBISJSOO666y6efPJJ2tvbufnmm4HJCSD8ruBXwdAUDBERGc28efN8/U2BFC9VQIiITIIbboCXX87tc65fD3fcMeLd1113HXv37uXyyy/nIx/5CHv27OH9738/jz/+OD/72c+49dZbeeSRR1i2bNmQx/32t7/lwx/+MC+++CIA+/bt4/LLL2f79u188Ytf5Ec/+hF9fX2cd955fOMb38AYM+TxixcvZsuWLdTX17NlyxZuvPFGfvrTn9LT08MnPvEJXnnlFWKxGLfccgtXXHFFbn8mI1AAISIiuZeHD/ebbrqJRYsW8bGPfQyAW265BWMMzz//PO3t7USjUW699dYTPmD37dvHhg0b2LFjB319fVxzzTW89tprrFy5kr6+voyH99WvfpV7770XgGuvvZYbbriBnp4errrqKg4ePEg8Hufzn/88V199NTfffDOPP/44wWCQd7zjHXzlK18Zxw9ECp16QIiIFIZ77rmHp556iueee44nnngCgPPOO4/LL7+cDRs2jFiJuXLlSiKRCHv37mXp0qU89NBDXHXVVQBcf/31/P3f/z0AH/zgB3niiSe47LLLMhrPP/7jP/K2t72Ne++9l46ODs4++2ze/va3U1VVlYN3OzoFECIiUhA2btzIDTfcMBBAPPzwwzz11FN8+tOfpra2lpaWFs4991wuv/zyE74h8Nx9991UVlayfft2tm/fzplnnpnRa2/dupX77ruPX/3qV1hrOeecc7jwwgvZu3cv8+bN48c//jEAx48fp62tjUcffZSdO3dijKGjoyM3PwApOKqAEBGZBKN8meFHV111FQ8//DA333wzDz30EA899BAAzz33HLfffju9vb20tbWxevXqjAOIn/zkJzz++OMDX4CEw2HeeOMNVq5cOWnvw1PwAYTXhFIf3iIiUygPH+5nnHEGTU1NHD58mObmZurq6mhsbOTTn/40zz//PCUlJRw6dIhjx44xd+7ctM/x/PPP88lPfhKAtWvXsnbt2oxe+4UXXuDd7373wDcH73nPe/j5z3/OJZdcwo033shNN93Ehg0buOCCC4jFYpSXl3Pttdfyrne9iw0bNuTmByAFx6uASCQgHodAIL/jERGRqXf11Vfzvve9j/e85z0YYzjllFMIh8N87GMfY8uWLSxcuJBbbrmFcDh8wmODwSCJRAJgyP3WWh555BFOO+20KXsfnqJpQqkKCBGRwnfllVeyefNmHnroITZu3MgDDzxAc3MzW7du5eWXX2bOnDlpP6BTjVQdMRprbdrtp556Klu3buX000/nb//2b/niF79IMBjkxRdf5L3vfS+PPfYYl1xySdavJ8XBq4AAfZEiIlKIampq6OrqGnWfZcuWEQgE+NKXvsTVV18NDIYJ9fX1dHd3j9jLavHixWzduhWARx55ZGD7xRdfzL/+678OHL+89NJLE34vmVIAISIiBWPjxo08+OCDbN68mSuvvJLjx48ze/ZsQqEQzz33HPv37x/18W9+85t54IEHANixYwfbt2/P6HXf/OY389hjj9Hb20tPTw+PPvooF1xwAYcPH6ayspI/+7M/48Ybb+Q3v/kN3d3dHD9+nEsvvZQ77riDl3PdK0MKhlcBAeoDISJSiDZu3Mg///M/c8YZZ7Bnz54R97v66qv53ve+N9D/YebMmfzFX/wFp59+On/yJ3/Cm970prSP+4d/+Ac+9alPccEFFxBIKaP7/Oc/TzQaZe3ataxZs4bPf/7zuX1joyj4KRgKIEREisfq1avp6upi/vz5NDY28oEPfIDLLruMs846i/Xr17NixYpRH/9Xf/VXXHPNNaxdu5b169dz9tlnZ/S6Z555Jps2bRrY/9prr+WMM87g6aef5m/+5m8oKSkhFApx991309XVxRVXXEE4HMZay9e+9rUJv28pTKqAEBEpHPv27QNg06ZNbNq0CYDzzz8/o2U4b7zxRm688cYh22699VZuvfXWE/a9//77B65fcMEF/O53vzthn4qKCr7xjW9kPvgcKvgAoqTEzZnUB7eISHF45ZVXBq7X19fzy1/+Mu1+3d3dgCtP3LFjB+A+kB988MGMX8s7mAD4zGc+w2c+85kh91988cVcfPHFJzzOW05LZDSpFRA6jhERkUJQ8FMwwFVBqAJCRERE/Kq9He6+G1LbiSiAEBEpHh//+MdZv379kD/33XdfvoeVcwVfAQEKIEREZGLOOecc+odNwv/ud7/L6aefnqcRSaH55jfh5pvhHe+AZcvctt5eqKiAvj4FECIiE2WtHVej6aly55135nsIJxipyfZEKIAQEZGc8fuH+3j96le/mtLXm4wPfPG3n//cXba3D27r6YG6OhdAqAmliMj4lZeX09rayqxZswryOGUyWGtpbW2lvLw8p89bFAFEaakCCBGRyaYP99yYrA988a9EAn7xC3c9NYDo7YV58+DwYVVAiIhMxIIFCzh48CDNzc35Hsq0Ul5ezoIFC3L6nEURQIRC+uAWEZls+nDPncn4wBf/eu016Ohw173LeNxVPcyc6W7rOEZEZPxCoRBLlizJ9zCEIgogVAEhIjK59OEuMj4vvDB43QsgvCU46+rcpQIIEREpBFoFQ0RERCSPXnhhMGjwAghvBQxvu3pAiIhIIcgogDDGXGKM2WWM2W2MuTnN/VcYY7YbY142xmwxxvxRyn37jDGvePflcvCZUgAhIiIifvXCC3DRRRAMqgJCREQK25gBhDEmANwJvBNYBfypMWbVsN2eAdZZa9cDHwG+Nez+t1pr11trz8rBmLNWWqoPbhEREfGfAwdg/3644ALX78FrQulVQKgHhIiIFJJMKiDOBnZba/daayPAg8AVqTtYa7vt4JphVYCv1g9TBYSIiIj4kbf6xR/9kQsbVAEhIiKFLJMAYj5wIOX2weS2IYwx7zbG7AR+jKuC8FjgJ8aYrcaYj470IsaYjyanb2zJdQd1BRAiIiLiRy+8ANXVsHbt0ABCPSBERKQQZRJApFvM/YQKB2vto9baFcCfAF9Kuet8a+2ZuCkcHzfGvDndi1hrv2mtPctae1ZDQ0MGw8qcAggRERHxoxdegD/8Q9f/QRUQIiJS6DIJIA4CC1NuLwAOj7SztfZ5YJkxpj55+3Dysgl4FDelY0qpB4SIiIj4TVcXbN8O55/vbtfVndgDQgGEiIgUkkwCiF8DpxhjlhhjSoGNwOOpOxhjlhtjTPL6mUAp0GqMqTLG1CS3VwHvAHbk8g1kQhUQIiIi4oeVuVK1tYG1cPLJ7na6Cgg1oRQRkUISHGsHa23MGHM98DQQAO611r5qjLkuef89wHuBDxljokAfcLW11hpj5gCPJrOJIPCf1tqnJum9jEgBhIiIiCS91Vrbku9BwOCxSSjkLkfrAaEAQkRECsGYAQSAtfZJ4Mlh2+5Juf5l4MtpHrcXWDfBMU6YAggRERHxm3QBRDjs/gzvAaEmlCIiUggymYIx7SmAEBEREcZYmWsyV+RKJ10AAXD8+GAFRGWla1CpCggRESkERRFAqAmliIiIMMbKXJO5Ilc6wwMIr9qhvd0FEJWVYEzmxzHnnw8PPzw5YxUREcmFogggVAEhIiIifliZK9VIFRAdHW4KRlWVu51JAJFIwP/8D/zv/07OWEVERHJBAYSIiIgUPL+szJXKOzYJJjtypQYQXgUEQFnZ2D0gvOfylvEUERHxo4yaUE53CiBERESKni9W5kqVywqIWMxdKoAQERE/K4oAQj0gREREiptfVuZKNVoAkVoBkclxjCogRERkOtAUDBEREZE8GK0JpSogRESkEBVNAJFIuD8iIiIifjA8gCgvd/0e0lVAqAeEiIgUgqIJIEBVECIiIuIfwwMIcNMwhveAKCtTBYSIiBSGogggSkvdpfpAiIiIiF+MFkBk2wPCCyB6evSFi4iI+FdRBBCqgBARERG/ybQCIpsmlKAqCBER8S8FECIiIiJ5kC6AqKtzAUJPz/iaUIICCBER8S8FECIiIiJ54IUGwysgvFUwvCkYZWWZN6EEBRAiIuJfCiBERERE8mCkKRhHjrjrqoAQEZFCUxQBhJpQioiIiN+MFED09Ljr2TShVAWEiIhMB0URQKgCQkRERPxmpADCM94KiI6O3IxPREQk1xRAiIiIiOTBSE0oPeoBISIihUYBhIiIiEgeRKNgDAQCg9tyUQGhAEJERPyqKAII9YAQERERv4lGh1Y/wNAAQj0gRESk0BRFAKEKCBEREfGbsQIIVUCIiEihUQAhIiIikgeZVkCUlUE87v6M9lzf4s+5rOpZBRAiIuJbCiBERERE8iAahWBw6LbUJpRVVcCf/RlnvPo9YPQqiFgkwZ9zL+8OPaEAQkREfKsoAgj1gBARERG/SVcBMWPG4PXKCgsPP8yS158FRj+OiYfdtyyNoWYFECIi4ltFEUCoAkJERET8Jl0AUVo6OPWiKnYcolHKo53A6AFEIuzunG0UQIiIiH8pgBARERHJgyEBxJ49cM01EIkM9IGo7G4CoDzSBYwRQPS7g5xZiWa6uoY2pRQREfELBRAiIiIieTAkgHjySbj/fti1i7o613gy0OoCiNJ+VwHR3z/yc3kBxMx+95iOjskatYiIyPhlFEAYYy4xxuwyxuw2xtyc5v4rjDHbjTEvG2O2GGP+KNPHTgUFECIiIuI3QwKIJhcccPQoM2cmp2E0eQFE5hUQVX3NgNU0DBER8aUxAwhjTAC4E3gnsAr4U2PMqmG7PQOss9auBz4CfCuLx046NaEUERERvxkSQDQ3u8sjR5g5M7kChhdAhMfuAWH73Z3BWD/VdCuAkKLV0gLW5nsUIjKSTCogzgZ2W2v3WmsjwIPAFak7WGu7rR34p14F2EwfOxVUASEiIiJ+M1IFxOrVcOqpg9uCfZlXQAA0UByNKG+6CX7843yPQvyktRUWLHAzmkTEnzIJIOYDB1JuH0xuG8IY825jzE7gx7gqiIwfm3z8R5PTN7Y0e98C5IgCCBEREfGbWCxNAHHkCLfdBs88M7gt1NcJ2FF7QNhIcQUQ1sIdd8APf5jvkYiftLa6XikHD+Z7JCIykkwCCJNm2wmFTdbaR621K4A/Ab6UzWOTj/+mtfYsa+1ZDQ0NGQwrcwogRERExG/STsE4ehTjHT0lAwiTSFBJb0ZTMKA4AojublcR0t2d75GIn3j/RkYL60QkvzIJIA4CC1NuLwAOj7SztfZ5YJkxpj7bx04W78NdPSBERETEL0aagjHA2wbU0DV6ADGsAqLQV8Hw8hoFEJJKAYSI/2USQPwaOMUYs8QYUwpsBB5P3cEYs9wYl9cbY84ESoHWTB47FUpKIBBQBYSIiIj4x0AAEYkMrpt55MjgDikBRC2dGQcQ84KFXwHhBRA9Pfkdh/iL928kHM7vOERkZMGxdrDWxowx1wNPAwHgXmvtq8aY65L33wO8F/iQMSYK9AFXJ5tSpn3sJL2XUYVCCiBERETEPwYCiJYWt6G8/MQKiAUL4OBBaujKuAfE/NJmXiqSAEIVEJLKO9ZXBYSIf40ZQABYa58Enhy27Z6U618GvpzpY/NBAYSIiIj4yUAA4VU6rF4NW7dCX5+7o7UV3vIWOHhwzAqI1DsbQ808WyQBhCogJJWmYIj4XyZTMApCaal6QIiIiIh/DAQQ3tn02rXu8ujRwaqI5cuBsXtADHzLYgxzTPFMwVAFhKRSACHifxlVQBQCVUCIiIiIn5xQAZEaQFRVuevLlgFj94AYOMiZM4f6vsIPILx8RhUQkko9IET8r2gqIBRAiIiIiJ+MWAFx5MhgKJFhBYSJJu+cN4+6WOEHEKqAkHRUASHifwogRERERPIgGoVgEBc2BIOwYoW74+jRwQAipQJi1JMq7yBn3jxqI8UTQITDEI/ndyziH2pCKeJ/CiBERERE8mBIBURDA8yZ49YOT62AWLQIGwhk3gNi/nzKoj30H+/LyYl5OAwf+ADs2zfx58olL4AATcOQQaqAEPG/ogkg1IRSRERE/GRID4iGBggE3KVXAREMwsyZUFMzZg8IExusgABooJnjxyc+xt//Hv7zP+G55yb+XLmUGkBoGoZ41ANCxP+KJoBQBYSIiIj4yZAAYvZst7GxcTCAaGiAkhJMbS0zzBg9IGKDPSDABRC5mIbhndznIszIpZYWqK9311UBIR5VQIj4nwIIERERkTwYMgXDCyDmzh2cguFtq6lhRsnoPSBK0lRAFGoA0d8PXV2weLG7rQoI8SiAEPE/BRAiIiIiUyweB2uHTcEAF0B4FRBeAFFbS+0YFRDEBntAgAsgWlsnPk6vusBPAYQ3/cILIPxWAfGLX8CTT+Z7FMVJTShF/C+Y7wFMldJS/WckIiIi/uCdKJUTdl/np07BOHbM9X9ILsFJTQ0zOD5qABFIMwUjtU/CePmxAsJ7X0uWuEu/VUD80z/Bnj1w6aX5HknxUQ8IEf9TBYSIiIjIFPOOSWZEkmfTqVMwYjF4442hFRCZNqGcNQsbDBZVAOG3CoieHjh40FW4yNTSFAwR/1MAISIiIjLFvGOS2nByuU1vCkZjo9OHuaIAACAASURBVLu0dkgPiGrbNXoPiHiUqAm5ZTzr65ljchtAdHZO/LlypaXFXfq1B0Rfnwsh/BTaFAsFECL+pwBCREREZIrFYu6yui9NBYQnpQKi2o5RARGPEi8JuesNDcwrbaapaeLjnA49IPwWQPT2ustDh/I7jmKkAELE/4oqgBi1eZOIiIjIFPG+FKnpG1YBMUIAUZXoItI/ck1/MB4hbkIDzzU3UNhTMAIBWLDA3fbbFIy+Pnd58GB+x1GM1ANCxP+KJoAoLVUFhIiIiPiDd0xS1TusAsKbgpG6raaGEiwlfSOfaZfEo8QCpe5GQwP1trADiFmzoKrK3fZrBYQCiKmnVTBE/K9oAghNwRARERG/8I5JKnuaoKwMamrchurqwTPrlAoIgGBf14jPV5IyBYOGBupihR1ANDS4dhdVVaqAyLeXXoJHH833KJzUKRhqAiriTwogRERERKbYQADR1eTOpo0ZvNOrgvCmZSTDiWDvyJ0gA4nIkACiKnqcjqaJzz31Tu77+vxzHNXSAvX17npV1SRUQDz77ISetNh6QHzta/DhD0Mike+RQLC7gxZmcSE/9c3vq4gMpQBCREREZIp5xyQV3c2DlQ6euXPdmbVXCZGsgCjtH7kCIpCIkkgJIABCnS0T7n+Veh7ul5UwvAoIcAUjOa2AaG2Ft78dvvOdcT08Hh/8Fr5YKiC6utyf/fvzPRKo7DrGLNpYwU5NwxDxqaIJIEpL1YRSRERE/MELIMo7mwbPpj2LFg12WISBCojS8CgVEPEo8ZQeEAANTHwaRmoA4ZdpGKkBRM4rINraXO3+sWPjerg3/QKKJ4Dwfv7bt+d3HMBA98lKetWIUsSniiaAUAWEiIiI+IV3TFLamaYC4stfhh/8YPB2sgKiLDJKBYSNkggkKyCSzzebppwEEIGAu+6HACIedxnBpFVAeGUeHR3jergXQAQCxTMFw/v5+yGAMBFX9lBBnyogRHxKAYSIiIjIFPOOScqON50YQMyfD6tWDd5OVkCU9Y9cARFMRAYDiGQPiUaOTDiA6OkZXBnUDwFEa6srUEgNIHJaAeEFEO3t43q41/9hyRIXlHi3C5kXQGzblt9xAJj+wQoIBRAi/lRUAUQi4ZJzERERkXyKRqGSHgLh3hOnYAyXrIAoj45cARFMRIkHk1MwkgHEPA7npAJi3jx33Q8BREuLu5y0JpQTDCC8CohTTnGXxVAF4acpGF4FhAIIEf8qmgCiNPmZrCoIERERybdoFOpInuSedNLoOycrICqio/SAsFGsVwFRXU2iuiYnFRCpAYQfmlB672fSp2BMsALCCyCKoQ+E9/PfvTv/S6KWRFwFRAV96gEh4lNFE0CEkp/JCiBEREQk36JRKCXZHbusbPSdy8uJlwSpiI1cARGyERLB0MBtM6+ReWZiAUQi4U6o5893t/1QATE8gPBbBcTwAKJYKiCWLHFTY159Nb9jKYmqAkLE7xRAiIiISFEwxgSMMS8ZY57I91iGBBBemeZIjKG/tIbKWPoShEQCQqRUQACmsZGFwSM0NY1/jN7JtJ8DCL9VQAyfglHoFRDWut+T885zt/M9DSMQVQ8IEb/LKIAwxlxijNlljNltjLk5zf0fMMZsT/75H2PMupT79hljXjHGvGyM2ZLLwWdDAYSIiEjR+xTw23wPAtzxSAhvKYwxAgigv6yWykT6CohYzD1XIpTyPI0Tr4DwKgvq6qC83F8BxKxZ7rKqyp0AJxI5eoEcVUDU18PMmYUfQPT1uRBizRoXBuW7EaVXAaFVMET8a8wAwhgTAO4E3gmsAv7UGLNq2G6vAxdaa9cCXwK+Oez+t1pr11trz8rBmMfFCyAikXyNQERERPLFGLMAeBfwrXyPBYZVQIRCo+8MRMpqqbGdaZtpe2GGTZmCQWMjDbEjNDfZcY/RCyCqq10fTL8EEDNmDGY21dXuMmerTXgBRDjMeJoIeBUQlZWwYEHhBxDe70hNDZx+ug8qIGKDFRDqASHiT5lUQJwN7LbW7rXWRoAHgStSd7DW/o+11ouK/xdYkNthTpyaUIqIiBS1O4DPAiN+V26M+agxZosxZkvzRLs3jiGrKRhAtKKGWjrTHsfEYsnnGhZAVCR66T02ct+IsXhTG6qr3Um/H5pQtrYOVj+Aq4CAHPaBSH2T46iC8IKQigoXQBRUDwhr4dZb4Y03Bjal/o6sXesCCDv+zGvCgpqCIeJ7mQQQ84EDKbcPJreN5M+B/0q5bYGfGGO2GmM+mv0Qc0NTMERERIqTMWYD0GSt3Traftbab1prz7LWntUw1tKYEzRkCkYGFRDRilpq6EpbyTlQATFsCgZAsPnIuMeYWgExY4Y/KiD6+111gcergMhZH4gJBhAFXQGxfz98/vPw8MMDm7zfkaoqF0B0dOT3PQdiakJZMOJxlzhKwckkgDBptqXNNo0xb8UFEDelbD7fWnsmbgrHx40xbx7hsZP6rYMCCBERkaJ1PnC5MWYfrpLzbcaY7+VzQNlWQMSSFRDpTqq8HhBDgoxkAFHZeWTcxz6pJ5d+CSAikaE/Li+A8GMFxPz5cOxYAR17eh1Njx0b2NTTA+/iCdY8eTtr17pt+ewDEYoPLsOpAGKa+/a3YeFCeP31fI9kRNu2uUwun1U/01EmAcRBYGHK7QXA4eE7GWPW4uZVXmGtHYirrLWHk5dNwKO4KR0nmOxvHRRAiIiIFCdr7d9aaxdYaxcDG4FnrbV/ls8xDYQGkFEFRLxy9AqIUiLYNAFEI0doaRnfGP1YARGJDP1xTcoUDO84dAIVEN4UDGvhyPiLUPxlhADiGu5j2Q9u5/TT3bZ89oEIxgcrINQDYprbvdv9g7rttnyPZEQPPuhmJSnsyk4mAcSvgVOMMUuMMaW4D+7HU3cwxpwM/AD4oLX2dynbq4wxNd514B3AjlwNPhteWq4mlCIiIpJv2VZAJKpcBUS645i0FRDz5gEugBhvYWnq/H6/NKEcqQIip1MwFi1y1zs6sn54b68bXzDoAggooGkYXgCRsrZrdzfM5SjBzjZmVMVobIQ9e/I0PgYrIDQFowB4yel998GBA6PvmyfeDBH9rmVnzADCWhsDrgeexi1d9bC19lVjzHXGmOuSu/09MAu4a9hym3OAF4wx24AXgR9ba5/K+bvIgCogRERExFr7U2vthnyPI9seEPHqWqrpJhI+sYfmwHOl9oCYMYN4aTnzODzuAMKvFRCpAcSkVECcfLK7Ps4KiIoKd71gA4hhFRBzOYqxFlpbqanJ4YokWYrHoRQtw1kwmpthzhxXRnT77fkeTVptbe5S1TbZCWayk7X2SeDJYdvuSbl+LXBtmsftBdZNcIw5oQBCRERE/CLbCghbVUMJlmhHD1Az5D6vAsKUpgQZxhCf3UjjwfFXQAzvAdHVBYkElGRSPztJIhE3Fs+kVEBMIIDo7R1skjk/2bK94AKI1AqILstcjrobzc1UVs7JWwARiUA57kywjAjRvhgZnuqIH7W0wKpVsHQp/Pu/w9/93cDUMr/wKiAUQGQnjx8hU0sBhIiIiPhFNAplJvMKCFtTC0C848RlNWNRS+nwKRiAaWyc0BSM7m4wxn2j7530d41/Vc+ciEYnsQIiEnFnErNmQXU17Xvb+ad/ct+sZyq1AmLmTDe+lFUrpzev8qGpySVRQLStiyp6B7ZXVuavAiISgTIGyx5iXX35Gcg08OyzsHhxDoO7ydDcDPX18Ld/61LWr3wl3yM6gQKI8SmaAEI9IERERMQvolGoCGReAWFqXdVDoqPzhPui4Zjbp3RoABFc6AKIlC+ss9LT406gS0pcDwjI/zSMSe0BkUxXbE0t3aV1/PiBdj73Ofjd78Z4XIrUCghj3Je3e/fmYGx+4P0ixWKD1SFHjw7e39xMZWX+Tmqj0cEKCADbqwBiJK+95lZVHe//DVPBtrRwKFLPj3cu49iai+h/+rl8D+kECiDGp2gCCFVAiIiIiF9Eo1AWSB6UZBBAeAlA4viJJQiJcPrnMfMamWcmVgHhneB7FRB+CCBSCz28k/2cVEAkl+D8j+/XsqetjoaAO8nO5uQitQICYPly18y/IKSerSavB1uODtnmpwoI25OngUwD3vlQn18zmngc2tr41g8b2LAB/ntbPc17Tgxf8ynZ9gRQAJEtBRAiIiIiUywahYqSZAVEBlMwzIxkAJGmAiLW656npGzY8zQ2MsMe5/jR8Z1ldHcPTnHwUwCRmrMEAu6EPyffuicDiCd/UUvlvDrOWp59AJFaAQGwbJmrgEic2Dt0+mlqciUdMDAdI9Q6tAKiqiq/AURqBYTpUwAxEu98KF9/V2Nqa8NYS0egnl/9CmYsqKUi6q8Aord3cPULBRDZUQAhIiIiMsWiUSgvyXwKRsmMZOPJNE0YEv3u4Gb4FAyvYVvi0JFxjbGn58QKiM48nwMMDyDAjTGXFRCd1DJreR1lvS6AyGY1heEVEMuWuccfPpyD8eVTIuHm5J9+urudDCDK2pMBRCjkiwqI6TYF49gx+Pa3p/51fR9AJJfgjMxs4OyzIVBXS2U8zw1ohvFWwAAFENkqugBCPSBEREQk36JRKCvJvAllsMad1ca7TzypintTMMqGnZnPmwdAoGl8AYQfp2As6XuNdUeGruheVZX7AKJqQR2h7olXQCxf7i6n/TSM9nZXFr9mjbudnIJR2XmUKEH3RpM9IPI9BSMeKgemRwXEAw/Apk0D59tTxvdTMJLzxuxJ9QAkqmupIOyrEzlv+gVkH0C8/vqQ1WyLTtEEEF5argoIERERybchFRDBsZcKLJ3hAoh03+p6AUS6KRgApW3jDyC8KRh+aUJ5Q88/8rEfvRNuvnlgeYrq6txOwQjW1VI2ZybBrtxUQADs2ZOD8eWT1/9h5UrXlTR59lTVdZS20ByYMyfvFRBeE8podR0AJWH/BxBeQdNU/7vyzuP9XgFRMqfB3a5xFWC20z9VEBMJIK66Cj772dyOZzopmgBCUzBERETEL6JRKC1JLp1pzJj7l810Z7WJNAFEIpzsATHCFIya7iPjOkH3YwVEWaKPhCmBL38Z3v1u6OrKeQXE7OW1UFdHINxLiMiEKiAWLnT50rQPILyvaxsboaFh4HZt71E6yubC7NkDFRD9/dktXZorXgVEtGb6BBDev8upntoU7Gzjw9zv+wAi1OgqIMxMl4D2HvVPH4iJBBDt7UMfX2wUQIiIiIhMsWgUyk2ahgYjGKiA6EkTQPSPUAExaxaJQJBGjoxrCkBPD8wLHIP6eip/8wKBQH4DCGshaCMcnbMO/u3f4Mc/hi98IecVEAtWuQACoI72rAOI1AqIYBAWLy6AAMKrgJg9e6DaAWBm+CjHK+e6UCJZAQH5+Wbd6wERrz0JABP26/yCQd7v7VT/uzr91Qe5n2soOXRgal84Q7bJTcGoPNkFEAEvgDhWGBUQkUhx941QACEiIiIyxaJRKDXRjPo/AJTXJc9q00zaHgggyoeFGSUlxOrnMo/D/P732Y+xuxtW9m6F1lbMi79ixoz8BhCxGISIkgiWwsc/DueeC1u25KwCovdYJwkMS9ZUDQkgsp2CkVoBAQWyFOfwACJZAXFS5CjdVckAor2dmnL3u5ivAKKMfuK17u8uGPHr1/uDvJ/TVFdAlPe6Doq2xZ9fw/cfbqGTGmbNKwMgeJILIPqOFUYFRDSau/4bv/sdvPpqbp5rqhRNAOF9weCj3iUiIiJSpKJRKMuiAqK8KkCEUPoAIjkFI1B+YpgRmN9II0fGHUCc3PNbd+ONN5gxI7+rYEQiUEoEG0r+zNasgR07qK6yOamA6Hijk05qOW2FGQggZtKR8clFLJZcXrVi6PZly1wFhLUTH2PeNDVhjeGtV86i2cx2AUQ8zknxJnpqklMwgLq4K53PZwVEYsb0CSDyVQFR1tfhrrS3T+0LZyhysJkW6pkzx90OneR6QPQ3+yeAaGsbbN+TzwqIv/5r+Mu/zM1zTZWiCSBUASEiIiJ+kW0FRDAIYcrTlpWPOAUDCCxoZGEw+wAiHncHyPM6d7oNBw5QW5vfCohIxFVA2GDyfa5ZA62tNJYcy0kFRM9hF0CsWMG4KiC8bGh4BcSyZe7nlrps37TT1ESirp6f/jzA3t7kFIzWVoLECc9MVkAAM6OudD4fAUQ06iogEnVuCkYgMn2mYEx5BUTYBQ+ms2NqXzhDsWMtNNPg5VqUNbgKiGirfwKI1laYO9ddz2cFRHd32tWZfa1oAghjIBBQACEiIiL55wKIzCsgAMKmIm0AYSPu4CZdBQSNjcwbRwWEd2I0p21oBYQfAghSAwhgWd+OnFRAhJs76aKWxYsZVw8I76R7eAVEQSzF2dREpM59Hd1kZ7s3m3xD/Sc1DlRA1Pa7qRp5qYAIJyglikl2TA1F/V8BkdUUjHAYDuSmZ0N52AUPgeP+rIAwzUMrIMpnJwOINv+cabe2utwtFMpvBUQ0Ov36SRRNAAHuF0QBhIiIiORbLAalZF4BAS6AKOlPUwERGaEHBMDSpcyMtdC8K7uv372KglnNyQqIYQFEezscPZrVU05YNJqcglGaMgUDWNztAohEYmLPH2/rJFJR68qqkwHELJN5ADFaBQRM80aUTU301bqQ4XDcnRXGtm4DIN4wWAFR0+cqIHLSFDRL8V5XqmIqK4gEKqZFAJHNFIxXrvkqXUtOJ9Y38ZOZin4XQHhLzfpNsGNoBUTFHBdAxNr9VQExaxaUlyuAyFZRBRClpeoBISIiIvnnnUxnUwHRX1KRvqy83x3cBCvShBnr1wPQ2LwtqzLv7m6op5nynlaor4fmZuqr+jh+3J00nXcevOc9mT9fLgxUQHihTUMDzJ7NvLYdWOsCgP374RvfGN/zm+5OqHEnOl4A0RDMfAqG92328ABiyRJ3Od0DiO5Kdzb4Rr8LIOK/cQFEYvZgD4jq3vxVQMR73FlYoLKMSKiSULywpmD0vbyLmvhxfvG91yf8upURF0CEuv0ZQJR1t9BKPfVuEQyq51QBYDumfwARjzPw/1UuTMcVNYoqgFAFhIiIiOTDZz/rlmP0jCeAiATSBxDeFIx0PSC8AOIMXspqCkB3N6wgWf3wx38MwKLAQTo74frrYefOqa+A8JpQDqkaWbOGOS07AHcy95d/Cdddl/2c6GgUSsOdA932KS2FykpmBbKvgBgyBeP736fi6OssWDDNA4hjx+gsdyHD6z3Jr6W3uwCCOXNcYBMIUNGdvx4Q8V73F2Uqy4kFKyiLFVYFRGXHYQBe/O6uCb+uF0CU9vowgOjtpTTaS29l/UCTx5oZJXRSk98uuMOMN4DwvgxXBUSRUAAhIiIi+ZJcuRBwxyOhLKdgRAIVBKIjBxDByjRhxuzZRBvmsZ6Xs+oD0dMDK0n2f3jHOwCYnzhAWxvcfz/U5OFcYLACIuV9rlnDrKOvYkjwX/8FTz/tNmd7Arx3L9TSOTDXHIC6uqymYJxQAfH883DVVfDVr7JsmQ96QPz0p/De92Y/VyUchs5O2oMuePh9p6uACO18hS6qKa+vhpISqK+nvDN/FRC2z5WqBCrLiJZWUhb3fwCRTQ+I2u5DALT9cteEe7FUR1zwUN7nwwCixa2kEp3ZMLCpqgo6qcV0+6MHRCLhpqGNJ4CIRqGBJkrDx3OyMk40SlZLBfuBAggRERGRSVZT4w5SYzF32wUQ2VVARIMVBNMEEN5XammbUAIlZ67nDF7KKoDwKiDi5ZVw/vkANEbfAOCCC1ylwVR3XvcCCFM6tAIiFO5mEfv53OcGN2d7ArxzpwsgqucNDSDqTParYFRU4OqsP/lJt2H37oGlOPPqwQfhBz/IvnSl2VU1NJe4AOK1FndZ0tvDUeZSVZXcr6GB0uP5q4BIJCsgglXlxEsrKadv4N+bX/V1x2mgKaMA4qQ+F0Asi+3kkUcm8KKJBJUxl2BUhH0YQCR/3xIn1Q9sKimBblNLoMcfFRAdHW4axUknja8C4jH+hK/ymZwEB14AMZ2W+S26AEI9IERERGSq1bhl7AdO2qNRCNnsKiBiwQpCsTQBRPLblbQ9IIDAWWewkt+yb2fmR8nd3a4CIrLkNDj5ZABWVb/BunXwn//pKu4jkan95m1gCkbZ0AoIgDXs4NAhuPBCtznbE+Bdr8WpoZu6RcMCiHGsglFZCXzrW7Btm+uNsHs3y5e78/58NGccsC05ZeLgwewe1+SqGo5ZV/nQGyvFzpgJwFHmUl2d3G/2bILt+auASKRUQMRLK6ik19ffDCcS8L6+b7OXpcTaxjix7u6mOu72WV+xi+98ZwIv3N1NAFcFU9XvwwAiWQFRMrdhyObeYA3BXn8EEK2t7nK8UzDmcIwFHMzJ1Anvy3U//64PV1QBRGmpKiBERERk6qULIII2uwqIWKiC0jQBxKg9IADWrydInMT2HRm/Vk9PsgLilBVQVgZz5rAkeICXX4YFC6A2eZ4+ldMwvAqIktQKiNWrARdALFoEn/qU25ztCfD+V92yH8OnYMxMZF8BURVph899Dt78ZvjIR2DfPpYvdl/F792b3bhyJpGAV15x1w8dyu6xyQDicGz2wKboLBdGDK+ACLTlbxUM25esgKguJ15e6fsAIhyGtWynmh7q2sYojzns+j+Eg1WsLNnFz34G+/aN84U7OgauVkX9G0CUNtYP2dwXqiUUnv4BRDQK5YSpoSsnjSi9c9vp1AeiqAIITcEQERGRfBh+wj6uCohQBaXpOvvHkgc3I4UZyUaUM15/OePXCrf1soj9mFUr3YaTT4Y33hi43wtUpjKA8Bp3DpmCUVtLdN7JrGEHX/oSzHRfzGd9Anx4Z+fA8w2YOZPaRPYVEHPvv81NEP+Xf4HlyyEWY2nQ/eyyPffPmT17Bn8o46yAONA/GED01bjrR2gcDCBmz8Y0NVFamqceEOHBCohEMoDw80lZTw8sYj8A9Z2jJ1P2oPvF2X/ym6nqaWYm7Xzve+N84XYXOhxjNjUx/wUQ0cMuxKpcNLQCor+0lrJ+f/SAmGgFRBn91NKZ0woIP/+uD6cAQkRERGSS5aICIl5aQWnixADCePNLRwozli6lv6yG5d0vpX75OarSfb+jBEtozQq3YeFCOHBg4H7vPH0q+0AMVECUD/2Zhdav4apVO/jgBwcbQGZ7AhxvTxNA1NVRG89+FYzyV7fA2We74Gf5cgBmtu4Zss+U27598Po4A4h9vbMHlkXsrhqsgBiYgtHQAMePM6MikpcAwvQn/6LKy7HllVTQ5+sKiNQAYk7v3lHn8If3ugqIIyvfBsB7V+/iqafG+cLJ/wReZwm18XbfNQ/ofaOFGAFqF84Ysj1SXkt5dPpXQEQigxUQCiCKgAIIERERyYd0AUQgkV0FRKKsgtJEmqNMrwJipOcqKaFr6bqsVsKoPuBWwAitHVYBkTxZycsUjH5LKVHM8Kkma9YQ2r0TotFxBxCl3jerwwKIyng3sXBmnQy91wx0tLqTcYBlywCoPpbnAGLbNtfJb9687AOIY8egooIjnVWceqrb1FGWfgoGwMLy5rxWQLgAwv89IFIDiMWJvaOeQIb3uAqIjjNdAHFBw05efXWc2UFKABEilufGJCeKHGqmlVnMaRx6mhqrqKEy5o8Aoq3NXY53CoZXAZGL/w9yvaznVCiqAKK0FM47+DB84xv5HoqIiIgUkbRNKBPZVUAkyioot2kqIKJjBBCAOeMM1rGN3bviGb3WjCM7iVOCOfUUt+Hkk92JSrJ8Ox8BRLTPBQElZcN+ZmvWuKPw3bsHToazPQEu609fAQEQ6smsbMQ7mShpb3VnJuBO+MvLqTy8e1zjyplt2+C001xFxngqIObMob3DcEry16E1uSLG8CaUAPNL8xNADFRAlJVhK6dBD4iWbmbhzmSXsnfUpTVj+w/RRTWsXQuhEGtCu+jogCNHxvHCyQBiH4vd7XZ/TcOIHWuhmQbv12lwe2UtVfFOX1RstLa6PG/GjHFUQPTFCRFzFRB9E38vqoDwubJgnL/a/Rk3J09ERERkiqStgMiyB4Qtr6CUqFviMYWJjjEFA6h983qq6aHlV5mtBTmr6be8EVjqGlCCm4IBA9Mwhr+fqRDrG6HZZnIlDF55ZdwVEKMFEOV9mZ2g9fa6H5dpa3Pr84E7S1m6lLKDe8Y1rpzZtg3WrXMdRMcRQNjZs2lvh8ZGqK6Go8kVMY4xl/Ly5H7JCoh5wab8vM/+wQoIU+n/KRjxva76IVpSylL2jhrm2UOHOcR86hqCsGwZC/t2AfDqq+N44WTgsJel7rnb/BVAmJYWWqhnzpyh2xPVtW71jryVEQ1qbXX/PZSUZB9AxHvdL2WQOP0dE3sviYT7A1oFw7fO6Hqe2ZFDvvjFFRERkeKResJubTKAiGdXAUFFhbscdhxjYlHilEAgMOJDQ2ef4a689FJGLzW7bSevl68Y3JBcitNrRJmPCoh4nwtaAuXDAohVqyAYhG3bxh1ADMwtTw0gkk9mwpkdN/b1wczysHtxrwICYPlygvvzWAHR0QH79w8NILL5Frm5mfhJDcRi7qRr9mz4ec2l/PL0j7KvajXGJPdLfmU9J9Ccl6r+1AoIU1lBFb05+YZ50ux3AcTBBX/IIvbT2T5ydVLg6CEXQNQBp53GSU0TCCCSFRD7WQRA5Ji/AohgR3PaCoi8/KczgtaUIqdsA4hYz2BSEGufWIKb2lqg4CogjDGXGGN2GWN2G2NuTnP/B4wx25N//scYsy7Tx06ltx97wF3JW/wsIiIixSg1gPAKGIKJSFYVEF4Akeg5MYCImTGeZ9UqYiZI7d7MVsKoCrfQUT53cINXAZHHAMKrgAhUDAttyspg5Up4+eWBjCbbQ73KdAGEV/2R4VeLvb0wrzxlcrhn2TLM3j2Azc93YF4DynXrYP58N10ludRhRnp7iQTd3Ja6OlfosKt3Ifed+w1Ka8oG90tWQMw1+amAiSP6bwAAIABJREFUKIkMNqE0VS48ina5bT/5CXz725k9z7/8C/zf/zsZIxwqcMgFEM1r3kop0YE+D+mUth7mMPNcYc2KFQRe/z2zT4qNO4DoNLW04DqK+i2AKOtq4XiwfiBM9Jha959otHWaBxDdgzsnOib2Xgo2gDDGBIA7gXcCq4A/NcasGrbb68CF1tq1wJeAb2bx2KnR38/5Rze76wogREREZAqVlbmsoatr8KCxJB4dVwXE8LJdE4sSNWM8T1kZR09axbzmbRl9+R2Kh0mUVQxumDPHvYHkFIyqKjBmaqdgJPqTAcTwCghwJ9fbthEKuWFmc6hnLVTGkycCXlIEeHMLBk5sx9DXB3NDyfb43hQMgOXLMX19LC0/kp9D0G3b3KVXAQHZrQcaDtOPCxpmznQ5Q3Ozawky0IDSuzMYpN7mqQdENBkUlZVhqt3Za6zL/Vu54w740pcye57Nm+E735mMEQ5Veng//ZQSOes8AOyeEZbiTCSo7HBTME46CTjtNEw0ykXL9o07gOhgJv0VbopRtMlHAUQiQVW4lXB1/Ql3lcx04WDvUX8GEJkWFSX6BgPNeIcqIEZyNrDbWrvXWhsBHgSuSN3BWvs/1lrvt/d/gQWZPnbKPPkkVdHjbC97k/tU8kEDExERESkeNTVDA4hsp2CUVLoT4uEBREksMnYFBNC9cCXLYjsHlpAbTWm8D5saQJSUuCqIZAWEMe795GUKxvAKCHAn14cOQUsLlZXZBRCRCNTSSSRUOXQayzgqIAYCiGEVEAArS/fkL4Cor3cNHLwAIps+EP39hI373fOmYDQ1uQBioAEluN+R+nrq48fy8j4DKRUQgSr3uxvvcgM5ejTzxR4ikXE2d8xS2bH9HGAhVevcUq3m9RECiJYWAvEoxwLzXQZ52mkA/FHDrvGthJEMIOK1LoCItfgogGhvp8QmiM5sOOGu0CwXQPQ1TWHqOYK2tqEBBAyuRjEWrwcEgD2uCoiRzAcOpNw+mNw2kj8H/mucj508DzzA8fLZ/HfFZa5bR6a/JSIiIiI5MDSAsAQSsaymYJQkT6oix4dVQMQzmIIBmBUrWMw+du8Y40jVWsptGCrKh25fuHCgAgLcbIUpDSDC7mg7WJHmva5f7y6TfSCy6UHQ358MIMprh96RPLMIRMMZneT19kJDIDkFI7UCIhlAnBbYnb8AYt06lxqNM4DoS7gwxpuC0dwM3d3DKiAA5s7lpGh+AogSrwIiFCJQk6yA6BwMILq7M3seb4ZK6sndZKhu3sd+FnHSuoXECFB6aIQA4vBhALpq5rl+G8kAYm3ZLjo7sytmAaC9nQ47k5KZtSQw2FYfBRDJqUF21okVEF4A0d/svwoIyDwAsH2DO9rOiYUpqaezhRZAmDTb0v43bIx5Ky6AuGkcj/2oMWaLMWZLc3NzBsPKwvHj8MQT/HrJ1XTaZGmdpmGIiIjIFEoNIEIkz26yqYBIBhDRzmEVEBkGENVnraAES9Mvfj/6jsmjWlM+LIA4+eSBCggYfD9TJREeoQkluBNsGAggsjnMC4ddABGtGBZAJCsgSunP6GS0rw8aStJUQCxaBIEAy82eqe8BEYvBjh2DP585c1yVRzYBRDhMX3xoABGNuvPiEwKIxkbq+o/mpwIiGqa/pByMGQggEj19xOODFRveigGj6e93VQVNTZM73pq2/exnEXMXBHmDk6k8MkIAkUwYemYmv8OdNQtmzWJpZCeQfSNK29FBG3XUziyhg5lpV8H4zndcbvbaa9k994QlU6LSWTUn3FVW77ZFWvIbQPT3u98lL2PMNoBIrYCY6H+ghVwBcRBYmHJ7AXB4+E7GmLXAt4ArrLWt2TwWwFr7TWvtWdbasxoaTiy7mZAf/AD6+/n1aR+gOzHO9sgiIiIiE+CdsMdiKQFEFhUQger0AUQgHiFWMnaQMfsC981p7292jr5j8kg2WJ0mgDh0aKCL5lRXQHg9IExZmvfa0OCmGLz8ctYBhFcBERseQCTPLMoJZ3Rw39sLJ5GmCWUoBIsWsTSRhwqIPXvc3+fate52IADz5mVdAdETdz+LmTMHFrvg9deHTcEAmDuX2r78BBAl0X6iJS4oCdYkG7Z299La6n5lrc1sITzvW+VJnYbR309N9xEOBxdRVgb7SpZS0zJ6ABFpSCkiX7WKhqOvAOMICZJTMGbMgHbqBpblBPfzufZa+PCHYe/eqQ8gEr3uH1p1ffkJ95XPdv8+Y235DSBah2WME6mAMN2agjGSXwOnGGOWGGNKgY3A46k7GGNOBn4AfNBa+7tsHjslHngAli3j8Pyz6YorgBAREZGpl1oBUUryLCeLCoiBAKLrxAqIeMnYQUZo9alu/9+NHkB4XdpDNcNOAhYvdmdyyeUD8xVAjBjarF8/rgqIgQCiauQAIpM2EH19cBKtrnKiomLoncuXc3I0Dz0gvJPL1DUNvaU4MxGLQTxOd9Sd2M+YMbDYBeFw+ikYNb3HCPclMqo2yKVgPEykxP2dhWa4433b0zskSMhkGoYXQBw9musRpkhOZTpW7pbCPFS2lLr2kadgJDAkZqesSvOmNxHa8RLz6iPZN6IcFkCUHB8MIDZsgP/4D9i0yd2eygongM4m939PTcOJAUTFHPfvM94+zQOI8OB/JiXduauAyLBVjS+MGUBYa2PA9cDTwG+Bh621rxpjrjPGXJfc7e+BWcBdxpiXjTFbRnvsJLyPkR0+DM8+C+9/P6FSM1gBkZd1kERERKRYeU0bh0zByKICIlTrNdYbXgGRWQBBVRXHyk+m+tCuUXfrbnFH0qW1w04CTj/dXb7ivnmd6iaU3hSMEUObdevgt79lRkUk6wCihi4SlcO+zk9OwSijP+MKiLp4cnK4GTYLedkyFvTvnvrDz/7/n703j5LkLM98f19mRO5LLV1dvUtqLd1oR2hBIPbVBhvwdox9OXg7xozta4PHgMG+NrZlz6C59tjHNgye8XbxcjDGgAVICIMwiE0LSEJCC91Sd0ut3qoqK9eIjIyI+8cXkWtsmdWqbkvfc06fqsolMiIyIjvfXzzP+w4mQ/Q1DYDwnt+wslSr0kAxzDKCHBBpp8cCq5u+remeSS8tt3MYQBw7BkKW8Il6g/i77Gl1QHgQ72RBAojjhb1UjJPBhOTJJzmV2kp1y9A5ft11YJq8fs99IwAitgi1bUS9PgogGjVAOkS+9CX41V+Fm26SDz+bAERpMUsXHWf9zDahrMndxbzs4Tk1gBiegpFuKwdEqFzX/Yzruhe5rnu+67o3erd9yHXdD3m//5zruvOu617p/bs66rmbqn/6J3lG/eRPoutQVw4IJSUlJSUlpTOgjTogfFu53ZzNAQGwurSfbesPRTZVbJyU32Sz1bEi4JJL5M/77gOkA2IzCxS3m8ABYVnssx+cGkDkMHBzY66FGRwQFXt1tAGlrwsuoNyrodVXk69YmOp1WYDeeWf8Y/3L+R6AcF1g504JIJJ01vQBRDfH3Jy8aTgpHeSAANjO5o8c1XsGVlq+Z5k5D0C0Oxw7Bn/MO7iV10zlgNgMALFalgBipbpX3v7YY5OPffLJwQhOX9ddB8ArSt/gwQflW/mBD8hzMrIp5fo6ADXmqFQkgNAa0gHRaAB2j2utO5j7wHv5NN+P+2Rgcv5pk9UIgZ9AuSJoUMbdTOoZIG8XUq3Kn9MCiOEHam3VA+KZqZ/8SdlJZd8+OX+7pwCEkpKSkpKS0ubLL9hHAMQsDogxAJF2utjpZCDDPG8fFzoPc+J4ePHpA4jc3FgRUCrJznSeA2KzIxixAMJrtLjPvHcmADHRdHMGB0TFWhnt/+DLm4SxtP695CsWpm9+U/77xjfiHzvkgLjpJrjwQrB37JJd9PxKKsHz141s/4pvEgCxjc3vA6HZJj1Nvod+w1bRkQ6I6/kaF/PgWQUgHASNqpxKUlvwAMTByRiG8+RRnnB29Pc/IPuxLC9zhflN6nX4rd+Cd79brvvRKGbgXb5fY77vgMg0JYBYXXG5i6t585/fgHbTH/L9fJbFh796OrY2sfweEOliAIAoQ50KonFmAYT/mVfxEltTA4ghmql3lAPimanlZXjLWwB5kaHpKgChpKSkpKSktPna6BSMTNVrrNcaBxDJHRD6pfsp0+TQ18KrlNYpufz8Qn7yzssv7zsg/O1JciH9dMg1Y1wjF14I+TwXNKcDEIYhAcTE2FEPQCRtQtnpQMlcDQYQu2VP9rn2abiifM898ucUAOGOu7K8+92yJ2VrzhvFmWR+o7fhwwAil5PvPQREMLZvB84MgNBtA9uLYFCQ3/d9AHE+B1hgNRGA8OvDp7UHxKFDrGR2kCnJY7m5NRxA8ESAA0IIuO46dh+VEOrGG2Gvt4jI/e4BiOEIRqa9Bq5L85GjXMm9HHj9r/Qho1jf3BGddstrgFue/OzJZqFBhXTrPzmA8B7YEkV0U43hfMZL16GNAhBKSkpKSkpKm69yWY4BrNdnc0Bk5+SXcrc9CSCcdLLlzD1/PwCrXw1vRNleld9ki4uTVyG57DJ49FHodKhU5PZs1lcq14pxQKTTcNllnLv+7dPjgBACW88mimBYluzXWDBWgiMYnm2gbJyGUfPf+pb8OQWAeOd7Mmia97SyByCS9IHwnr/WGUQwYOCCOJscEBnHoKd776EPIIwO64dqLLJKHoPOSvRK2XZ/yMvpd0B84QvwiNer/9Ahjurn9Pdfess8dVGZBBCmSWr11CSAALj2WgpHHmYxtcbLXiabR0JyANGPYNhd6HSwv3EXAPXX/phsOAuk65sLIPzPtiAHBEBLq2w4trBRnS4HRE1bImsoB8QzXgpAKCkpKSkpKZ0p+VeNV1dnc0D4PRnGAYQ2RQRj+cVyFGf3vnAA0alFAIjLL5fU4cEH+1/ANy2G0U3QN+OKK9i1ci+tZnJbhmnKmEWqMLm9biabKIIhGy665DshDogtWwAom6cSr1eopnBA9Nqy2Ok4WT7wAXnbamEGANHOjkQA/EaUEw6IUgk7V2Abx2IbPn74w6d3zKPmmNia54DwppCkjDbpQ4Oi3joe3YNj+IryaQcQP/ZjcP318O1vw+OPcyR9bh9AVKqCx8XeSQDhrUQggPD6QHzjz+7k058ecK/IEsebilJjjnwe6un5/u36fXdjk0K/5kooFrHQ0BubCyCcTsgEHk8dvYK+waJ9o6rXJev0GNfUAEKY8oG1zBJZa+M9IP4f3s//yzsVgDhbpevQwbP0KAChpKSkpKSktIkaBhCzNKHM5QUdcridSQeEm9ABoe3ZQVOU0A6GT8Iw1uQ32dKWEAABcN99/e3ZPACRYHLIhRdSNFbRjEbiMZCm4ZILBRC5RBGMdhuKtOTV5CAAkc9j6kXmeifj1+vIEfjhHx6M0BxWozG4ip4AQBx9TB5n7/qtLFd7LeJPatulhT8JgPA2fLU1CiBCHRBCYC1uS+SA+MVfhP/1v+JXIamyjoHjOyA0jS46abNN4diB/mPsk8kARCYjIxinLV5Ur8v5jaur8IpXwJEjPM7AAVGtwiFnF+54Awfv76fYPtoDAuCaa0AIzj/1DfL5QUEcOX1kqAdEJgPtzABAlB6+m+/yHOZ2FEAImpoXz9hMeQBCKwUDCDNTJmueeQBRqQwG3UwNILoS6tWzS+S7G3dAvIwv8oN8SgGIs1XKAaGkpKSkpKR0phTogJgigpHPywspYqzC0NzkEQyE4GhlP/PHwh0Q5rr8JltYCCgC9u6VK3LffX0HxKZNwoiLYEC/Is7TSfyF3GrKgiBdyE7c52alAyIugtHpwCIr8o+gCAbQKS2xxMn49fr61+HjH5f/xnXvvYPfkzggWnLFd+7N9iMUa62M7JE2hQOiZiaMYAD20rbYKRi9nvx34kT8KiSV7prY+uA9NFIFUt0O8ysDAOGcSgYgdu+WvwcxoJl0+LD8eeONcqfZNo/Z5/ShQaWCnPDQGGtS4b3Ha8xPHlbVKuzf329GWkhS4gxFMHQdOrkBgFh4/G7u5nn912lm5sl1NjmC0THokSZb1ALv7+Yq5DZYtG9UPoDwNSuAaOa2kOtt3AFRoM0unsA0NqkZz2nQswpAZDLKAaGkpKSkpKR0ZrRRB0Q2632PMUe/6epOF0dLDjLWt+9nVyt8FGe3LpcvxpsygvQeX3op3H//pkcwhJVgn3nW+zydxF/1/MZ3qaDceTa5A2IBr7gNckAAZlkCiMgr1DCoZD71qcn7/PjFxRcnAhBORxY7WnHgYKjVgF27pNMiTh6AMEkYwQDc5e2xDgh/E48fj1+FpMq6Bk5m8B6aqTyi3WaHORRrWI0GED5o8logBMYwbr0V3vGOKVfOG7vJy14Gt98Ob3oTt/ZeMYhgVKBJaRJAeF0zG5SDudZ110kA4bqJAYSbStGkhK6DWfDe1AceoNQ8zr3p5/mnEJ3cPHmzNuWGblCGgUEu9BS38hXyGyzaN6r19WAAkWRUL0Cqa2CSwcxWKdobd0AUaZHDJNM8DSN+N0nPKgCh62Cj4WYyMf4kJSUlJSUlJaXTq406IDRNAoiUMRbBcC0cLTnIsC/Yx273CMcOBIf0rYZXHY43ZfTlTcLY9B4QvQT7zKvCpgEQvaZn+w4CELnT54DoVrawhVPx6+VX57fdNvl99Vvfwl1e5kj5Odir8cWhY8gV14uZvoOhVkP2pEhyed9bl3EAEeWAENvjIxj+/jxdAMK2ZR8Pd8gBYaYL9OptzucAzXnZ9yJdW4lcju+AOOcc+TNoEsZHPwp//udTrqDvgNizB/buxfnYx3nAvGAkgtGkxMSYDu/vJiWq1YDlXncdnDoFBw9SPHAf38dnYntA9IpVXFKjAOLznwfg0crz+tECIz9PqbvJEYwYAGEXKhSc1qBT6BnQRh0QKcukK7JY+TIFe2NjhHwHBEClnsDRdJboWQcgAJxcQTkglJSUlJSUlDZVG3VAgLyqmzJHi1LdtXCncEDkr5STMI7e/kjg/XbTW34+YAwnyEkYJ08yZ8rqMXEEw7bhXe/qj/ibVqkpHBAF2lM7III674vc6XNAWPPSAZEYQHQ68O//PnKXe889PJh5Lrd+Yw7jeLwDwjW6OAgyBY1CQUKsWg1JDqaYSWkwGsHYuxdSqYETYlipndtYYA2zHk5t/E08XREMy5KTTJzs4D3spgs4LQ9APOcaANLrySIYPoAIckA89ZR8veGGlbE6dEgWIt6YUp8rDUcwmpRIdVqMNAnx3qN0pUQ6HbBcrxEll16KfvUVfIbXUTwU0dmzVqNXlG+kroNV8gDEF76AQ4qjW6/sP7RbnKdsby6AEKYEENnJNBQATsn7EN203NekxgGEv65JAUTaMuimcvRyZTTsDY2v6HYHAKLaTDBW9yyRAhBKSkpKSkpKSpugjTogwAMQ3bEeECRvQgmw+EIJIOrf+G7g/X5BHumAAOaO3CeXk9QB8fd/DzfdBP/8z4nXdVjiaXJAOO3wzvsinwxAjDggQgCEvTBlBCOXG4lhuB0D5zsP8okjV7FOFb2dbAynSZZMViAEzM15AKIUcLU95Pkw6YD4wR+Ehx6CnTsnn6LvlqM4o+wN/iaurMheEBtVtysdEGQGlaul5Sk76+zmCO4ll2KKbKxN3XdmRAEIv09kkt3X1+HDsrFESpZe/oSQ8QiGcN1R14v3ItnFgKwLSBj44z8Ob34z/NqvAaCtRYx6rdXoetBB18Ete5X02hqHi/vJLQ4sLb3SPFVn7fQ14kwg0TXokA9njOXNtl1NagRAHDtG+pMfR9end0DYxY1vy7ADYqGtHBBnpfyD2c4qAKGkpKSkpKS0ufIBxMrK7A4IK50j3R13QHRxpwAZ219yES0K6Hd/LfB+vyAPXbfLLgOgdCAZgDh2DD70p12Ovf13AHj8GzP67pMAiFkcEN6oyqDO+yKfLILRbsdHMNzFJQp0MFZi5lP6lcz3fR/cfHP/ivj/9+7vkHZ6LL5SAohMtxVbvbuGByC8t3IEQMTNyRxal3EAkUrBhRcGP0XbKQGEdiogvzC2ia4LJ0Pq5Ucegccfj19FkAAih4E77IDQCzyH75LGIX/J+dTTC2RbyRwQi4uSZQVFMHwoMRWAOHRIxi88jQOIfgRjfMGNBj2hUVoIORc1Df7xH+Gv/gp+4icASNUjojm1Gt3CwAGRLaRppGW24zuZ540cunZ1njlqdNqbRyBSZifSAUFVFu1u/cw6IPpxmBtvhB/5EarZeEjpK90zsVJZ7MLG3RxW16WIPJgWOsoBcVbK//9KAQglJSUlJSWlzZbfsG8kgjGlA6KbzqNZYwACC3eKHhDpfIa7yy/lvEduDbzfNQy66dxgzty4tmyB7dvRHrofTYsHEG95C3zrV/6abe3HMMnQODib7z7dS7DPZnBAuJ3wppupKRwQC6ziFErh4GbLFgB6T0VcoQZZnafT8EM/JKvdu+8G4N6/+RYAP//B59JMeRVQ3M43Tbpk+gVdH0DMEMGYGAMZJi9mkFkJsA94Gt6fQTGMRgNe9CL4pV9K9pLdjo1Oj+HK1dIL7ECuQ+mK82lkFsh1kgGIbFZuxrgDwrLgJSc+ysd5E61TU/STO3RoYKsg3AEBjL4vzSbtVIn5hZBzcVheRibdjAEQ+QGAKBRgPSXf2Lt53uh7PDePhk3zqc0r9lNdGcEIO8XTcxJAGCfOEgfE7beD67I1uz4FgDCw0jmc0sYdEE7HJIUEREtd5YA4K9UHEJm8AhBKSkpKSkpKm6p0Wn7hH4lgTOuA0EYBhON4y5oSZDx6/mvZ2XoUDh6cvLNj0NNC4he+Lr8c4TWijLuA1zxlcGP293Cvv5678i8iuzabA0L0LHpCCwcjMOKASHKBHwYAIuiyq8hlyYnkDghnPtj9AJBalp0b7eOnohdmGDJ+8X3fJ60G//ZvrK3BBY17MHJVUuefh5H1AETcJIxuhAPCNOPzD0MRjOEeEJHaJh0Q2bV4BwQEJzX+23+TYOJUzK7y5Y8bHY4N9fRBDxNt3/m0sgsUjegmlKYJP8lH2P9X72L/wokJAHH8OLydD/ImPsHi7/1qspWzLJnbGAIQfhni94AIdUA0m7REKcxUMyrvDdKjAMTaGuYYgKgJSR3uMK8eeR2xIG9vP7l5fSBSXQNT5EJPcW1eugY6x88MgOj15HtXqSCtO9/5DgCLej0xgNB6Jr1Utm+J69U2AHiG6tllSwGIs1IDAKEcEEpKSkpKSkqbr3JZFjmzOiB6Wh69NwAQvZ5c1jRNKAEa178GAOvToy4I0wTNNnD0GABx2WXw4IPMl3uxF/De+NQH2WI+ibjxRtrlZYqt2QBE2u5ip2OAzQxjOJ1ORM+LXI78NE0oF4L7PwBo273REWGZA1+djlyXxUW44Qb4kz8hc92V/F98hOaFzwUhsAoJAYTZxSQ76YDw7ThxlMbb8FQuG26LH5fXmbJQnw1AHDkCf/RH8vekF4eDJrfYGVndd0Qetm2jnV+k1I13QLydD7L7n27in791Pj/0nfeP9GQ49r0mL+QOnmIbWz/xYfjIR+JX7oknZNYkIoJRLocDiIabEEB4l+Uje4PUahh5CRYyGXm6rDGPm0pxR/vKEQdEeov8w3hq8wCE36AxTPqi3Ebz5JkBEP7xWKkg3Q+etujJHRCaLR0Qfv8N69QGtmXoQ25bT0Uwzkr59NdSAEJJSUlJSUnpDMjvAzGrA6Kn58nYQwDCtEnh4urTLWfpBRfyGOdi/OstI7evr09OEwjU5ZeDaXJJ5tHYIvEV6//CwYXnwctehjW/3J+eMa1StoWdigEt3iXlaXpAEAUgslmywowtLlot6YAQS/EAQpxKEMHw1+V974MXvYiV0rncymswf/nXAejmkwEIYYU4IPzKNy6G4TkgCvNJ6QOg66ymt1BsJAMQ4xGM975X1uuvfGXyeLzvgBC5wXraWXksHC/uBSEwCwtUevFNKIu0aF1yLQ+d+1p++dTvwO/8zuB1Pn87GSzeyt+ycvGL4G1vg+8GN3Ptyx/BGRHBSKfBzk0CCLfRpGaXk8VfNI12ukS2E+KA6Hah3cbIjTogvudeQO+519CmOPI6+pJ8nHlscwGEFQEgMltk0d5dOTM9IMIAxKI2DYAw6aWziIrngFibfVtEWx5I7fwCO1zlgDgr5V9k6OkKQCgpKSkpKSltvnwAMbMDIjMKIKy2DzKmW86+/YJbeC25r31hZJ5grSbdA24SAAFcIe6LLRKzdptGUfYFYHkrRbeF20yYjxhSyrFw4qZ9zOCAEGa0AyKHERvB8AFEakv4persTtkDIr2aAED4I1Bf/Wq4+Wb+4tWf4Cf0j7H8098PgF1KCCCiIhiQCEBYqQxz8wl6EAxpNbONUmt6B8Rdd0lTwTveAb994hd554n3JHq9XnOyj4cPIFbnzwegW1qgasc7IEo0sc65gFt+5p/5BG/A/Zu/7UdVCnd8jjZ5vsRL+I//8k/yffrN34xeuUOH5M+ICAaAKE++J/Z6kyYJHRBAS58jZ4YACO9YaWdGAcQvO3/C4x++DRjtn5pZljSid3LzAIRmef1nQpRf9lwDK2eBA+KLX4QdOwCYT08RwbBNbC1Lyutn0VudfVtERx5Iq1suYo51erVpOqOeOT0rAYSVKRA/A0lJSUlJSUnpmSIhRE4I8U0hxL1CiAeEEO8/E+vhNy+b1QHhZPJknSEHRGe2cZ779sGtvAbdaMLXBtMw1takA6JfAIdp/35Ip7nYvi/WAaE7Jo43HlHfuSxf56HpXRDaFBGMqRwQMQAi68ZHMFotWBSriJARnAD5bVW66KRrCXtADOmhh+TUCU2Tf7sVD0DUIvL+QKorm1D6h8fcnCx+rWzyCEZXZJM3oPRUy22j2pkeQPzjP8pN/413GFz34F/x5u7f4NjxUxiCHBBOTh4LrWUJIHqVBQp0ImsAH0CIcolt2+BveSvixHH43OcA2HHfrdzOS+mS5aSg/4AiAAAgAElEQVS+Q9o07rkneuV8ALF7d/+mcQcEhACI+nQAop2ZIx8GILzoTzsnF6br8nRp2TlOdCQZHX6fs9vkH/apTQQQPQMrHf7Zk1+S+8heO7MAYkvvmHS+/OAPAjCXSu6AyNiyx056buM9IHwAsb4kR9KYB/9zxDCenQBCUw4IJSUlJSWlZ5lM4OWu614BXAm8Vgjx/M1eCd8BkROzOSCcrAcgXFmU9dpyOWLK5ZTL8ND2l8umjrcMYhi1mgQQqYCJECPKZmH/fi7o3B8LIDLOoKdE4TwJIE49MB2AcBxIuxZunAMincbNZChM5YCYbGDYVzbhGM6mw7y7GjqCEyBfEJxiC5n1KSIYnh56SDIfX045mQMiZZl0Rbbf1M9vJNkieQTDFLnkDSg91QvbWTDjp2AsL49GMB55BC66CCoPfA29Z7CN47TvPxD7enbL61VRGOw3NyftBb09ewFw5uR70z0W7oLwIxiiVGT7dvg0r8OqLsLf/i08/jhbVh/hi7rsn9JsIp1Ajz8e/T4cPiw3dOg9DQIQ2lxwBKNJKTEAMrJzFLshAOLIEQDWyrIXhe+AAHjSq1uHD9/CTvmizurmAQi918GKaIBbntdoUcBZP7MAYuf3viR/ecMbAJgTyQGE7kgHhD5XxEHg1mbflpQhP+Tq2y8CoHdIAYizTv7/zV0FIJSUlJSUlJ5VcqX8b/a692/zBtx78gFENmXJy9lREx0C5GTzcuyaF5uwjdmcFAC7Lq5wX+kFcOugEWUfQBRjAATA5Zdzbj1BBMM1cL0OhpULJYCoPzodgLAsGVuxE4wbFYUCZS05gEh1ox0QumthdpzIZTi1Omkc2TgyROk0nBJL5OrRAMKoGTxwMMeaV/dZFhw4MAogxFzCCEavi5UauAJ8kFB3E0YwDAOTbD+xkVSN0jYWrWN9UBawWECmEoYdEI88Ip0efP7z/dusL34l9vWcjiREqcJgW928rK5TF0oHhDMv35vO0XAA0TUcirRIVUps3w4WGQ694M3wyU/CRz8KwPf2vhrwdt0VV8gnetMQAjU2ghOSAwjRbNCklBgAGbk5ir2QY8LrRbFaCgcQw6CjtL1Mj3Ssy+Z0SrMNehERjHIZ6lSgfmZ7QCze/0W5Mi99KQBVkkcwMo6BrefI5QUNyjjrs29L2vB6QOyUAMI+9J+jD8SzCkD4/zf3AUTIh6KSkpKSkpLSM09CiLQQ4tvACeA213W/sdnr0HdApLozQQPXs5X7NnI/giGm7AEBMoZxc/c18K1v9S9D+wAinQRAXHYZW5qHIovgXg+ymP2eElsukQCi8/j0AELHSjbtI5+noiWPYPR7QASNefBus9vRFoh0zRvvGAEgANbSW8g3owFEc8Xg6GqOT31K/n3woNyPwwAiX83I6Q4xACLtj/zz5Bey672EEQxT9pAY7lWQRO3yNrKuGbp+QQCi15Pb6gOIUxc+n1XmEXfEAwjfAZEedkAU5TbmL5UAQizKy/tmBIBwWh1SuKQqJX+aKPdc+lZpjfjd3+WYvoveBfvJ5YYcEAD33Re+cocOjUzAgMF10GHmpZXzOIgRAJHuSAfEMKiIklWoUnZCgMHhw5BKsZaXfQviHBClsqDGHOn1TXRA2LI4D5MPIETjzDogSnfdDi9+sXwDCwXKzhQOCNfE1rPkctCgnHzUS4BSpjyQOrtkBMN9Qjkgzjr1HRDpvIQPcX46JSUlJSUlpWeMXNe1Xde9EtgFXCuEuHT4fiHEzwsh7hJC3HUyblTijBpxQEwZmwAGvRk8AOEYXgRjBgCxfz/cY14s//AqEL8HhF5K5oAAOK/9HWw7+CGm6fWU8ADE0iVyRGP3iRPBTwhRtzsFgCgUKKWTOyDSVgSA8CvEmOqiDyBiwvo1bYlCO7oHhDAMDHJ9Y8pDD8mf+/YNHlMswjrVRADCSk8CiLVu8ghGx83FtgQZlzHnVe/HgvtA+Ltzzx7JvhxHJhl6Pbh0Vw3uuova1a/iDl5I9q7kDoj0kAPi0DU/ws/wf6heLYuz9FJ8BMNvjpquFFlakm//163nwSWXQKvFv6dfw/YdglLJ23W7dsmdeu+9IQt0ZeEf4IAoFCA1VIkVioK2KA3eE8chbbRoUkoMgKziHJUoALFzJ11HNhLxe0CAnBQKow6IdBpqYp50Y2MAwnHg/e/vJ0DC5bpkHNkfIUylkizaU80pi3bbhi99SR5kG7gAvb4O23gK/cDDffcDlQpld4oeEK6Jq2fJ5z03R3N2B4TmAQi2bGGFBcSTygFx1sn/f95Me2eximEoKSkpKSk96+S6bg24HXjt2O0fdl33atd1r15aWnpaXnujDohxALERB8T+/dBk1PZdq0FeJHdAAFzOfaF1rGlKB4Rf3GuFDGtiHk5M54DodmUEw00QwSCfp5hK7oBIW4bsheF3eByWt95OJ7q6yDS9ojbGAbGeWaLUiYZboisBxOc+J+umIABRKsG6mwxA2KnBPvMBxGo3eQTDcKZ3QJgL8QDiRt7Htd2v0OvJ4+7RR+V9z12/HRyHzgtfyVe4gfyhh/sNFMPktL0eEEPH7UvetED2F36G8y+QMSdtqwQQvRMRkzC8/ZGulkilJGP71rcFvPWtAHzKeDXbtzMAEELIB4U5IE6elBsbACDGXQ2FAjSHAUSng3DdqQBErzTHHDV6VkCRffgw7NmDNZT+GnZAlEqTTLSRnifT2hiAePhhOcn0H/4h5oGWRQoXJxP+2ZNKQStVQWtPCSD+7u8kMDjvPHkSvO1tM4GIeh2u4S75xwteIH9Wq5Ts5BGMrGtgZ3J9B0RqA24OzZTATKsWeZKdpI4qAHHWyT+pjJQCEEpKSkpKSs8mCSGWhBBz3u954JXAQ5u9Hn0HhOhuyAHhtDwHhOkBiOz0MGPfvnAAEdgPYVy7d2Pmq1xGeCNKo2Wj0xsZj1jLLpNZnR5ATBPBKIpObLrAV9oysMJy595+cDvRrtlsM5kDopFbomjVZKYkRCkPQKysyAELDz0E27dDtTp4TLEINao4azEAwu7S0yYdECtmsgiGa5q03dz0EYxt58sGe+96l2xgMSaz4/Be/oBr7/4LQMYwHnlE3nfugX+HQgFx/fP5CjfIG++4I/L1HEO+P9qQA+KCC+CDHxxwpcw2+d7YJ1bCF9QaOCAArrpKJpTct/0C6+/5Qz7BG9ixYwhAgAQQ998vL/WPy+u7EBTBGN+nhYJ3PvoL9n42KCfe/05ljjQOnZMBYGkIQPgfPcMAIqjRZVOfJ9veGIA4eFD+fOyxmAd6FXxUBAOgo1fQjSmL9o9+FM49Fz70ITm55MMflmM0p1S9DldlH5B/XHKJ/FmtUrQTOiBcV0bSMjKCUadCqrWBHhBdWcvq1QJPsAvtuIpgnHXyLzQoB4SSkpKSktKzTtuBLwoh7gPuRPaAuHmzV8IHEBlhzeSAEAUJILrrngPCa0KZyk4PM3btAjvnFaJeJ8laDQqikwxACMH6OZdzOeGjOM26Nx4xPygMW8Vl8o0ZHRBJ9lmhQEEkd0DI0X/RAKLfJyJEuY5XpMUAiFbBc9acCo9hpD0AIYTsDzo+AQM8B0QCAKHZJnZABGOlmZWXk2McEG5H9oCYNoJh7TiHH+Oj0tZw5ZUTl7/9qMPWQ/Jq8vHj8qGVCuS/+nl48YspLWS4i6slQPlKTAzDc6hoEdGh/JYiJhnclXAHhGjJ/eGPxHzuc6XJ5LFTZR75offQJTvqgAAJIJpNae8flz+CM6EDouFMAohpHBBORb7BxvGx48JxZAZiDED47+vRo8EAop2dJ2+cHgDh/wyVV8E72ejPHiNTIWNOUbSvrcmmpj/6o9L58Pd/D9u2wY03Jl+Gp3odLk8/ID88fSJYqVC0EgIIz+XhZmSsqUGZdHsDEYxuGwdBppzlSXain1AOiLNOygGhpKSkpKT07JTruve5rvtc13Uvd133Utd1f/dMrMdGHRDp0iiAcDfQAyKVgq3neyvkFTtra9IinLTi7Jx/mXRArAfbmc11zxo/5IAw55epGrM5IEjogMhPMYYz3TPDc+deBMM1oh0QGdMjMJVK5OPahS3ylygA0ZOTAK66Sk5IjQIQbi0aQOi2HPnnq1CQjoDauhirooPltI2ZmlAWCvAv/Ai12++FSy+VEYYhSuU3ESw8+ShVapw4IR0QLzz3ScRDD8ErX0mlAl2yHN9zTSyAcI14AFEqC1ZZQKyFA4hU29sfpQGAAOmCeMqbKhrogIDgGMa0AIISTmMSQCQGQN50FPP4WB+I48el62bPHnkujTkgTDOYnRn5eYrdjQEI3wCTGEBkoje2my2T607hgPi3f5PNRX7kR+TfuRz82q/BF74AX/968uUgD+H9zgMD9wNAtUrekhGMuFSH/zky7IDQOrNHMPRuGyNVIJsTPMEuMrUT/QlJZ7OelQCiI7yzzctPKikpKSkpKSlthjbqgEgV5Zdzqz4awUjlZugnAey4aDKCkXUTRjAA6zmXU6WOdeBw4P3dhjceccgB4S4ts+QcTxyRgCEAkQTaFArk3SkcEHZE4ztvP/RHdYYoYzbppfTgRpZDMsqeAyKip4HWM+jpOV77WvjqV+V7Mtz/AYabUEaPSNQcE2eob4YQ0gVRqyGr6Jg3wTFMDKZvQukXts2FPfKqc68Hq4PCXww13nsed/cdED9Q+Hd54yte0T9XHt95A9x9d/SFQ6+w00vh+79UglUWSNUiHBDt0fmYl10mmzHec490CQCTDohLL5U7NghAHD4sHzw2RzO0BwQl3Lq3YM+VZKRLge1JgpSal69jnRw7LvwoyDnnBEYwINgBYRbmKVtrszVufPvb4X3v64OHQ4fkYRAqry5zYxwQVqFCoVdPvk4f+xjs3g3XXDO47Rd+QRKXKV0QzXWb87vfHQUQlQq57jquG5mskuve9EBmNtt3QOjG7A6IjNXCSBfJ5eBJdiJcd0DKzmIlAhBCiNcKIR4WQnxPCPGegPv3CyG+JoQwhRD/dey+x4UQ9wshvi2EuOt0rfgsEkJ+iPQBhHJAKCkpKSkpKW2iBgBiYw4Iq+HlpTcQwQA45xIJIKw1L2++1kNze4kBhLhCXv1NPxDchK/X9BwQQ+MRtR1bmWOdowcTdm1DfrHPkLBxZz5P1knugMjYxohLYEQ+UIiYnOa6kLMadDOl2NcyKzEAwnXRegZOJsdrXjOoscIcEKlGjAPCMXH00W3rA4hiMUETytnGcPrFdbsNdslzhQw5IIZz79eKOzl8WBaoNzQ+C1u3wuWX96c0PLx0g6xcv/nNiPVM4IDwAIRWDwcQ6c6oAyKXg4svHjgghIDl5TEAUSzKhhNBAOKpp6RlQoiRm6N6QLhjDoheLv648pVakACidyoEQIREMCDYAdErzaHRix/XOq6TJ2WfhT/4A/Z++1/ksnqDaRuB8t7DOABhFyrorpVsmmG9Dp/7HPzwD4++B6US/MqvwM03h08wCVDp5GNkHWPCAZEz14c3IVT+56GbzfUdEBljCpgyJr3XxkwVyOXgCXbJG588+/tAxAIIIUQa+HPg+4CLgTcLIS4ee9gq8H8D/yNkMS9zXfdK13Wv3sjKng5lMtBGAQglJSUlJSWlzVcfQDCbA8IvsHwHhGtKu+2sAGLfZRksNE49JgvCTs37Up8QQGhXykmm2UfvD7zfv+KXLg6K4Nw5ywCcfCD5KM6+AyJJ1KRQIGcnd0DojhHe+C6BA8IwoEgTK1eOfa1uNQZADE0CeP7zB8dLEICoMUfaaIdfdrVt0jjhACJBBANj9ggGyLr1j/+3BBDtYwMAkW4Nfn9h5k6+9jVIuxYXPXYLvO51/fmUlQp8p3y9fGAUgPCK0Uw53AFRLEoAkWmEN6HUjFEAATKG8a1vSQfE1q0ywjKx68ImYaytBVoLohwQ4z0g7HxyAKFtkQDCXh0DUyEAIs4B0avMD7ZjGt18MzgO7nnn8TtHfpbvf47sQBkZw/Cr95jPHscHWo0EzoFPf1oeG378Yli//MvyTfjQh+KX42n51FgDSoBqlYzZJIUdCyD8z0ORy5LNelMwHDueXIQoY7UxtULfAQHEUJ6zQ0kcENcC33Nd96Drul3gn4A3DD/Add0TruveCcQYT868dB06eLhPAQglJSUlJSWlTdQAQMzmgNDK8juM3RyLYMwIIF54g6BBmWPfa+K60FlLVgT4Ku8os8Yc2sngcYu+U0MbGo9YukACiNoj0wGIDF1EQgdExk7mgHAcGTlxYgCEZhuBQw5A1ollGomuVLvz3mXmsB4Q/lXgTA5dlw37CwXpIB9WP4IBhHcAlcVOJICIu7Ld3VgE48/+DD56qywYW08N1lPreMXjvn1c5dzFnXfCC7mDbLsGr399/3GVChzrLsiVPhwc8wHZJNRBkCmGnweZDKylFsm2IxwQxmgEA+QkjGPHZAxjxw55WyCA+N73JvdnrTY1gPAbYfov4BSSAwh9SQIIZ3XMAXHokNyZ1WoogAhyQLjVGQHEJz4Be/Zw8h8+j8DlL2pvRsM6LQDC9T9Ew477YX3sYzIzc/31k/fNz8tsU8RxNa7dDQ9AXDx0Ld7r+1KmkdgBQU42mTW0KbYlQBm7jakVn3kOCGAncGTo7ye825LKBT4nhLhbCPHzYQ8SQvy8EOIuIcRdJ2Nm/W5Euq4cEEpKSkpKSkpnRv53Z31GB4ReCQYQ6fyMPSB2gKGVWD3cpNORDRCB5ACiPHbVdkz+F+5ha/zCfgkgWgeTN6L0HRCJmm0WCugegIhzNpsm5JCRh0B5EYwsZqjju9WSxYddiHdA5EoaqyyEOyDGirAPfAD+5V/6hoC+/AgGIMc0BMkcNLwb1jQRDNGdLYLhP/6v/xqcoizQuqcGRZbuN957+cvZYR2m2j3B67lZTjl51av6j6tUvNps9+5oAOGtZ1oToY8BaGYWyHfCAYRuevtjiA74jSjvuUfWsiD3f6cz1NPg8svlwfad74wuMMQBERXBEO2mXJb33rjF5AAisySPCXctIILhjQIdBhDZ7CCZEOSA8G+cABpRarVk7OGNb+RRey8/x//mnKe+wefEazj17Yir896xPzyyN0iiMhnpCZRpwmc/C2960+QJhNzFB5tLrB9IXnfubT/AannP4IMc+tMwqsRPwui1Bg4IkBM9gGRujgDlei26ngOixhyWnn/GAIigM3maoMoLXde9Chnh+EUhxIuDHuS67odd173add2rl5aWplj8dFIAQklJSUlJSelMacQBsREA0fIARHdjDggASiWax5qsrkIer0F3QgCh69ASQ1dtx2S35RdubSiCUThPAojukacJQOTzaHYX17Zjm8IZhgcgwnLn3n7IYUQCiBLNRFeqCwU4wRJuQgBxwQXw2tdOPmzEAREDIMaPs/n55A6IlGnMNIbTL6537oR3/LYssnprQwDCb7z38pcDcDV38Yb0zYiXvnSkuOsDiD175BjJEImh0aVRamcXyPbaoZZ3vdvCEDnZNM7TlVcO7vcdEP4q9nffZZfJnwkBRJQDImX35AHvA4hSPNjyla9maJNHjDcnHQMQ/iEhxKAPRBCASC3KG42npnBA3Hqr3L9vfCMHDsDH+FGO/eFfcy3f5Jf+8nL4138Nfl5CB0RqbvJ4CtShQ5ISPf/5gXf/wR/AHY8s0T2SzIll27DPfoCVbZeM3uEBiAr15ADCgyzd7MYcEFm7jeUBCBA0S9ukXecsVxIA8QQwbPzaBRxN+gKu6x71fp4A/hUZ6Thj0nVoOgpAKCkpKSkpKW2+8nl5MU5LOtFhTNk5WS04ngMCrweElp8dQGTmS2StBl/+siy0gcQAAqCdLg+a943Jbsnl6eWh5S1LAOEemw5AZOgisskiGCBhSlzCwDSlu8FN4IAIKy58BwQJCsV8Hk6yhHsiGkDEXQU+LQ6IBD0gUpaMYEzrgPDBycc+Btv3yYLRXh0UWRnTAxAveQkOgp/gH7jQfngkfgHJHRCprklXRE8gAejkvZxBSKRA7zbppEdBUqUitwdGHRAwtPt8MnF86Jh23cAIhu1F/kN7QPgLbjbpkR6Bd3EqFOSV8FQ9mQPCfw4ERzC0Jbnu5rEpAMQnPiEX9qIXcfCghBzz7/gpfun6eziinQc/+qOj+8mT00527GsL8ngyTsS4BoYmf4zr7/8efvM34QRbKZvJHBCNms1+HmJ91xiA8BwZSRwQ/uehPxXIzG7MAZF12lh6AV2X+7lR3P6MmYJxJ3ChEOI8IUQG+HHgU0kWLoQoCiHK/u/Aq4HvRD/r6VUmAy1H9YBQUlJSUlJS2nwJIb+v6u5sDohsSccmhds+fQ6I4vYyJZp88pNDAGKKS96GVho07xuT74AYGY+Yz9NKl0mvTO+ASLSdXkVVIL4RpR/BcMOAy5ADIgpAlGiO2rIjVu0UW3BPRPeAEIXo/Z8IQHQlnHKzkwCi0wE7FxPB6PUQrjNTBKNUku735z8fcotFHARObQAgct06PaHBli2sbn0Ob+Yf5R1hAGLPHjnGM4QopboGpoiHZmbRq7JXghtRZrtNDG3SyeLHMIZ7QMDQ7svn5b/h5TYakjaMAQj/mEwCIDrpEoVijK1jbBk15kg1h46JVkuuVwiAiHJA6Fvljd3jCQGEZckGlD/wA6BpHDwo2VE2C9nLLuKm1HvkPglwAPX84rwYfezrC/I8M04kcEBAf7t93XEH/MzPwEtfCqVzl8jZ7URTPtr3HyCHSfvcYAdEIgDRHo1g9BvXzuiAyDltrEwRIeRH1Xr+GeKAcF23B/wScCvwXeCjrus+IIT4BSHELwAIIbYJIZ4A3gn8phDiCSFEBVgGviKEuBf4JvBp13Vvebo2Jol0Hbp2Wp4JCkAoKSkpKSkpbbLKZQ9AzOCAyOUFHfK4Hc8B4QEIrTBbDwiA/JYSC3qTz3xmNgdEVy+hd4MLWf+qZqYyurxmYZl8PTmAsCwPQOSmc0AkBRBko8dwJukBkaoki2CcZAlORTsghseWBimTgVY6mQNCBAAIkOAosvDy1mWWCMawiiVBnQruUJGVtRoYehmEoHbhNaRxOLn1EjjvvJHnlstDDggIjWGkrGQOCLO0KH9ZDe4Dkem1MNPFiduvukr+DHVAACwujgII32Xh73BP/jEZ1gOiv+BGg5YoTQV/fAChN4ccEP4+8wrxbje5AyK3tYKDoHcqIYD48pfldr/xjQAcOAB798q79u6FY82h+axjshvyMy3OAZHZMtlTJFCHD0u72c7R1oV/+Zfy/fv4x8FZjJlKMyTrXtmA0rpo9giG/3mY8pry9gqeA+LEaAzkv/93+Omfjl0l8k4LKyPfwGwW1nLPHAcErut+xnXdi1zXPd913Ru92z7kuu6HvN+Pua67y3Xdiuu6c97vdW9yxhXev0v8555J6bo3rahQkPhXSUlJSUlJSWkTdcklkEvP1oQyl/OmeXVGx3CmcxvrAbGYa9JozAggsiWyIQDCNYLHIxrVZSqd47KJX6MBjzwS/RpeBOPpckCEbm9CB0SZBqlqsgjGCbaSWj0lrwSPy3tf08X4/d8rJuwBEQIgOqmifExYowzv+bNEMIZVLEKdCmIIQOStRt9+bl5+NQAr179+4rkjDggIBRBpy6Cbit9ndtWrskMARM5qYuiTIOllL5PjN/3hB1MBiDFrQWty0AYQ7IBoUZoK/mQy0hmjt4cAxNAITgiPYAQ5IMrVFDXmcFYSAohPflKeM69+NSDHbg4DiH4fvgDw5ccT4o793FZ53FirCQDEjh0ToPfECcm55ueht7BV3phkAMJ3vAkYz3nO6O1TRDCcjjeW2ItgtEtb6YoM/NzPwQ03wN/9HYYhAcRtt8WvUt5t0/MARC4Hq5lt8rgLo6VniRIBiGeSdN1zpBUKygGhpKSkpKSktOn67GdhsTSbAyKfHwUQfvG4IQBRLlMRMoM8C4Do5UpkeyEAohPsgLCXllnmOMeOOtKu/ZKXRL6GZdikcUgl2c4ZHBAibHt1HVeIyCaU7XWLHCbaXDIHxCHOQdh2cLd6Y3JqSJicsgcgaiETCvoOiFHQ5QOIVspb3zAXhPf8DTsgfADRHBSMBbuOmZHA5tyfexXt7By7/uuPTzy3UpGHuLnVc0CE9IFI9wysVLwDwpmLARC9Jt0AAHHddZKTXXih/HtWANFowK//uvx9fLRqEIBoMJ0DQghopufkOFNfMQAin5fP8y7kj6hchjXmk4/h/OxnZWPRQoFWS6YBAgFEkAMiIYAobClgk8Jei+mbcOjQRPwCJGvw5x1M44DQH32AxziX4vLY8TFDBMN3OFmleX7yqofg939fHpNvfStf/uO7WFuLbc8CrkvebWMPAYhTumfROctjGM86AJHJeP9X5/MKQCgpKSkpKSmdGVkbc0AIw3NAnIYIBqUSWUt+250FQNi5Enk72gExHinI7pEA4pF3fBC+9CX5hTnCmdrr+KAlwXZO4YDwp2CE2r6FwNGzkU0orTW57dp8sh4QB/EqsoMHJ+4PGlsapmxJx0gXQh0QvjvGb3jnywcQTTcGQHgb3EtlZ2FlfRUKEkCkW8MAotHPvxev2kfBWKN0w5UTz+1PXCzvlFVymAOiZ2IlcEC48zEAwm7RzUxGMGD0lEgEIHww5AGIAwfg+utlj8Y/+iPpqhhWIIBwpgMQAC19jpwxBiCGoghBDoi5ucBJlX0AkaolABAHDsCjj/bHtjz2mLz5/PPlz717oUV4BMOPJ8Q13axUBQ3KIz1FguQePsznH9nDF74wevswgEgty1/sp+InYRQee4AHuKR/TA7uKOCm04kiGD6Q9bcxn4eD7nnwvvfJ+EoqxfH/czMgD4HIMcKGQQpX9nLBAxDaNnmfAhBnl0YiGApAKCkpKSkpKZ0JjQexEyqblQAiZXrFes8DEBuYgkGpRKrTZtd2m2p2egDhFEsUnOBvy67/jXwsBrDjiq1sYYVrP/5unKJXdFKmUZgAACAASURBVEVkl23Da7Z5mh0Q3XYPDRsR0XPBzeQiIxjdVVmFZhY3DiC69eQAolSCllYNBRD9kX+5YADRcLxiMOxSqz9FI2xEaUL5DgitLQtG24ayW8fKjVdyk+oDCCMD27aFOiC0noGVjl9Pfb6EhRbahDLvNLEy8U6WWRwQP/RDcPQo3HILvOMdTIwM1XUw0oMFu80m6zMAiHZmjlx3fXA+Hj4s4YOmAcEAIih+AQMAka4nABC33ip/egDCP7x9B8TcHOiV8AiG0zYwyJLJRjfdLJfl8RTZuNFx4MgR7j61hy99afSuYQCR3i4jGOYTMQ4I16X01KM8xP5Jp4gQuKVKIgeED2TTBXlO5nJDE2EXFzGf+3z2H7iZalWeJ5FJCu/Dzc4OHBDHU54D4izvA6EAhJKSkpKSkpLSZmtGB4SmgUGelOeAEN6kAz2vzb4uXjX1upe12V71wMYUAMItlkjhBjoYhBEMNMQ2bxQngv+x44/ljUfDp7z7Dggtf3odEFbTLwiiAEQ2sgmlXZNWcH0+vnDN5+EIu3FSaTh4kP/5P+HDHx7cb64HjC0NUbEIzXQ4gLA8ABHmgFi3g6roIYWM8ZxWmgbNVIVMe72/2DINeoV4YNMHEH4fiBAHhGab9NLx61kqC1ZZwF0JdkAUnCa9bLADYmQ5YQBibU0WvzABIA4dgre8BV71qvDl9vLl/oLdRpMG5akBRCc7h+50B5Xt0AhOmAQQb3sbvPe9wcsql/2mlgkAxC23SNrgzSw9cEDe7AMIgK3nhkcwJIDIhfaDHV6nOhVEIwJAHD+O6HY5xDkjZoB2W/7zAURxuYRBlu6TMQBibQ3NMniSnZMOCMCtVpMBiM4oYMznGXnOVxdez9Xcza/8mAQIkTEMbx86uQGAOIbngFAA4uySAhBKSkpKSkpKZ1wzOiAAzHSedHfQA8JCQ9OTj+qbkDc+8gO/1eCd/2V6B4TwqjG3MfltWXRNHET/6mtfXkF031tu4u8efb68LQJAOIYXJ0jShHIKB4QfeYiaOuHmoh0QTl1ut6gkc0DYaLSXzoGDB/nTP4WPfGRwv9WQL5KtJnNA1EU4gLB9AJEL7gGxZiWLYIw7KGZRR6uQMev9xZZpYOenBBC7d4c6IPSeQU9Lts/WmMcOmeqQd1pY2Q04IBxnEL1YW5O5Bu/8arcnJ1+MyymMRjCaU/aAADBzY71BDh6cABDD7PP7vx9+9meDlyWnasyTaccACNOEL3xBuh88a8fBg/L9W1wcPGz5/PAIhtsx6JCP5bI+gEi1InpAeMfJYfaMAAi/1YMPIKpzghNsxT4WE8HwPpueYsdE81C5oGqyCIY56YDwua3jwE0Pvg6AV1mfAaKH1NgNzwGRH0Qwjrtb5f5XEYyzS6oJpZKSkpKSktIZlevO7IAA6KbzpK0BgOiS2VBG36+mKqkmCwXvG/QUXQeFN37SjyKM3GcamCI36Td/zWvgy1/mBX/zNi540Q55WxSAMKUDQmSmAxBRX+BhACD8giBQ2WwkgHDrXiFUStaEEqC+ZS/OgYM8/vgoP7CawU07g1QqyYkHoQCiHeyAyOfl9+FVM1kEYxoYFSYjUyHnAQjTlCMLndIUEYxhB0RA1Cdrt7C0+GPWBxCBUx1clyItern49zGTkftwAkDAoL/E2pqkPUJgWfKUj4MJoug9YAMAolvwCFOtBqdOSevFc5/bv3/cARG5PgJamfnRnhJBuuMOWS178QsYTMAYPvV3XSDfI6cxeWK6nU4iB0Q2C01RJt2OcEAMAYjjQ9N+JwBE1RuLeyLGAeE5CmqFHYG9MlLVZBEMDBObFJmCBLLDEYyvfAU+++RltBZ2ce6DnwaiHRC9dbkP3SEHRMvU5MYpB8TZpar/Oa0AhJKSkpKSktKZUK8nf84IIKx0Hs0DEKLXxUIP/FKcWMOXc0N6NkRJq8rnt08EOyAsEbAsTYMbbkCkBNe9dl7aoA+Hf2n2HRCJ9tkUEYxEnfdzucgIBg0PQJSTjeEEqM3vxfneQVx3lB/0pnBAFIuw5kT0gGjLfTbe1E8IWRefMpJFMMYBxiwysxVyvSbYNkbHpUwDpziDA6LTCWwgOWedpJFbil1esYiMYKwGAIhOhxQuTj4+ggHytAkEEH4fiLW1fvzCv8odBxNyxbRsLNpoIFqzAQirOAQg7rxT/n7ttYP7pwAQAJ3cPLptRjaJ5ZZb5Lk51FnzoYcGDSh9nXt+GoMsrZMBJ6YhIxhxp7gQ0lGjd+IBxHgEIwxApFZjAIQHR1uV7cHrNFdlLhGAMDCH+lwMRzBuvx2EEOhvej3b7r+NDGYkgPAdEG5+ACAMA9i+XTkgzjYtL8v3xFUAQklJSUlJSelMyBudOattwdKGAIRlYbER+wOTAELTJiMTEUp7AMI4NfltOWUZdGOaA+7aLXiK7RgH4h0QifbZDBGMqKaPIh8dwRAtb7uncECsVPeirZ6kRGOkl54PRPJzCfoZlGDNjndABLk75ubgZCdZBOO0AAi/4WSzibnaIoWLGxSmH9OEAwImYxitFjmnQzMfDyB8BwQBUx38KI2dj38f/WUlBRD+cRgHEwoF6KRLcPIkwnFmAhC9kt/kYx2++U1ZsV91Vf/+adNfRt7rUBk1ivOWW+BFL+qfA8ePSwfEddeNPmx+Xk7CsNbDAUQS9mlkKmTNCABx6BBmrkKdKsePD0wz4wBibg5OsBV9NVkEozMXDCCoVqmI+AgGXROTwVQZP4LhunD33bBvH2Te+Dq0TpMX8x/RDogxAJHNeqfstm3KAXG2aXlZvtGWVogmeUpKSkpKSkpKT4e6U1zND5Cl59F7vgPCoic2CCD8K/eNhjeXcjrLfWYhHECkLRMrFV1R7N4NR9lB70g4gLB9AJFkn3kAoqIlcEC0EwCIXHQTylQ7uQPCLyZPlGVnvvN4jHp9UCDZLYMuOoVyOnZZpRKc6s0OIE60kkUwovpjJFUvPyAJ1qrcXyLB/vIf0ndAwGQjSq+qbBWSA4j0+qSLwh+n2u/DkGBZTweAaKdK/SvYTUrTpKEAsMtjDoiLLx45Nqd1QFilGADxxBNw//0j8Ys77pA/X/jC0YcWCtCmEBjBEAkdECAdNdludA+I9aoEVoYxGJgR5oDINuIjGE2tij4X4o6pJItgjG+j/1Hb7cI998Dznge8/OU42Ryv49ORAMLxAURh0ANCOSDOUi3Lpsu0XOWAUFJSUlJSUjoD8gHEjA4IW8+RsQcAwmI2kNHXuANiSgChL4T3gEhb8eMRd+2SACJ1LBxAuMYU+0zTQNepaPEOCLcdP3UiFeOASLe97U5QUOs6pNNwLC8BxF4OYtuDr6T+JIAkV737EYx2e+CqGZLTkQBBL04eH3Nz8FQjWQQjsj9GQvWKAwDRW/MARIKmnfm83F+RDgivqmwWtsYurw8gmuuDaRX+OvqZ+sJpimDUajMBiJYYBRDTOiDciteEcm1NOiCuuWbk/mkBRK8cAyDuuUf+vOGG/k133CGvyD/veaMP7QOIZsCJaSYHEFa+Qr7XmHgP+zp8mNXioPGmX4+fPCm33XfWVCoSQOjddnTHx6NHOaVtnxzB6atapeKuY3Qm+5MMS4w5IHy4dOiQ5DhXXQUUCnSufSmv5PPREYy6XF+/b0gu552y27bJDQ7bN2eBnrUAomnn5adBQCMbJSUlJSUlJaWnTdYUV/MDZOt5MrZnj+91N+6AGAYQnc7UACK3GAEgembsdIKdOyWAyK2F24bd7pSxlUKBcgIHRL9IjwAQIi8dEGEAQjMactJHgkpRCFl0PJkdAAgYmBicjizCAjvtj6nfhHJ4AUNyTAltggDC/DycWM/K6j4mgnE6AIQzBCDsNXk5OjUXH8EQQhaJ9TrysnUmM+mAOCHt891qcgeEGG++wcAB4RZndEDMzcmpFxtwQOTz0GJjAKI/5uS++2TFPdT/wXVlC5ppAIRdiQEQ/rSNpcH+v+MOuPrqyVYyxaKMYLgBJ2bKTB7BsAtlOfo37Ng9dIjjuXP6f/qNKE+elKvpN8bUdVjPbB3cGaajRzmW2hE4ghOAahWdXj9CFSbRNQMdEL5jpA9szjuPbRyLdkB4EGcYQPQdEL1eYK+Us0XPWgCxbnlnc2xYR0lJSUlJSUnpNGqDDggnmyfjmuA4CPs0RDA26IDIL8nn2+uT35a1noGdjq4o8nlYL2wnZ6yHFhSuOWVsJZ+nlI53QDid+CaUIpcjL4zQCEbGaGBqpclJHyEqFGDVnacm5tivjwIIOtM5IPoAoj6Zh3c7Jj3SZPKTcY65OVirCbmQGAdEVDwlqdzyEICoSQdEqhrvgAAJIBoNZHEfNIrTKxydxWQAYpUF+cdYQd0/fsszAohUSgKHlRVZ6c8YwWi4pf42NShPDSD0cg6TDNx2m7xhyAHh97+d6qNnPiGA8MBHpyNNEePxCxg4IETAeS66yR0Q/QkqAcc9zSasrnJU29M/JYcdEEtjh0m7uDS4M0xPPcWTbgSA8O5IN4PjUL5SXa8JpbeNvgPiq1+VP/1hJdrSPHPUaDXDL5Q7Le+gKo1FMLZt66/z2apnHYDw35Na1zubVQxDSUlJSUlJaTO1QQeEmfeucP7+75M1G1ip0xTBmLEHRGGrfH4vAEDotkFPj19ed9EbxRn2pXnaxp2FAqVUvAMCD0CIXPQYzrwIj2BkrCZmJlnR6q0aTzwBB9y9XFGRAMKvo1wjOYAolWSBCgwmcQzJNaTdO+iK8vy8rCfdiSp6SB6A0Esbd0AMd5N01uW6agvxDgj/qf06c/fuCQeE641QdLYkj2AAEwW1H8EQSewnBAAIkDGMlZVBLMYryqcBEHW31Hdoz+KAKBQFNebgwAH5GXP55f37Zul/KxYSAggvn3DnnfJ1IgFEZ/LETHcNOuQTOSD6QCvguPePj8Ps4RzPBBEFIMxqDIBwXTh6lCO97ZEOCEgAICzpgPB7/A47IC68cHCa6Fvn0bDproT3uXA9AJEai2C427xGmWdxH4hnHYDwbTerhgIQSkpKSkpKSmdAG3RA3HHhT/HZ8o/Cb/82lx79HPZGHRCZjPznOyCm7HpX3JLHQeDWAxwQtomjxVcU7g4PQBwN7gPRj2BM4YAoigQ9IDwAEQldYsZw5qwG3Wyyq/neqnHvvXCQvZznjjog/CZ1SRhQUgARtMvm52WR6BZLsRGM0wEgRHXoirVHE7T55A6IPoDYs2fCAWEfP4lBtt8MNUojAGLMou5PwRCzOiBgACD8Yn0GB0TdHrz+TACigAQQIC+pDx0As7BPbXGop0SQajW5M7yq2r+a/4IXBK9biyLCCIhgdDuJHRAjx9O4vOPjoH0Oe/fKlJEfwThxYhJAWHNbB3cGaXUVul0et3ZE9oAASDUjJnPgTQUS2b4zwz/Pv/vd0X4ZqUV53DgrEZNHmi1sUqTzmZFldReUA+Ksk6bJz4aTLQUglJSUlJSUlM6ANuiAcCpzvH3ho3DbbRyuXsYjucs2vk5+NTWDA6JcETQp4TYCHBCOgZOJX17mnGgAMTW0yecpptqB9cmIjGQAIqwJpetCrtfEyk7vgDjIXhbrjyFw+uspTINuKkcqwTf0YlEWqECgi8E1TbpkQh0QAL1seATD74+RLW/QYQOk5mWB5q7XcT1Yoi/MACB275bHiJ8lAHpHT3CSJUrl+AhMPg+1EAeEP5khVT4NDogNAIhab2MAIp8fiuYENKCE6dhnqZqmRhX7VASA8PtOIK/m79sHW7ZMPrRYlA4IzZiEXmkreQ8IP77jrgec4IcOAfCouYeFBRm/j3JA9KM7YQ4Ir5A/SgSA8KwLvZVoB0TaMrHEYAOHWe/QpNT42AtAu02bAnpGHvf+R1h/VKhyQJxdWl6GE00FIJSUlJSUlJTOgDbogMjnvUnir3wl73zlffz2rr/a+DptAEDkcrJQEq3JQjbjmDh6fEVRuEACCOvw6YxgdEIvavoSZgIAkc2ScYObUBoGlGlg55M7IPyC8nDqPNK9Ljs42ndApLoG3VQyB0qcAwKzG+mAALAy4RGMXtvEIEu+kKy3RZT0eVlUW6t1REMWjfriDBGMPXvAtkeu7jrHT0oAkYABpVJg5EMAhOeASFWSOyBarbFhA6cBQDQYBRDTjuEccUAMNaCE2QBEtSqXZ52MBxCOIx0QQfELkJ9dbQqku5P1l9ZL3gPCj+90T4U4INJpHm3KqRXLy9IBYZryNBkHEJmFEqbIhgMID4o+RfQUDADrVAyA6EnA6Gv4Y2dkYoh33IhaNIBoUey/lz64MbSSPDiVA+Ls0vIyHG8oAKGkpKSkpKR0BrRBB0S/2RjyQrCfJ96QyuWZe0AIAe1UiVQ7AEC4Bk42fnlbL6zSIUfr0RgHxDQRjFSH48ejB54lAhC5HFk3uAllqwUlmtiF6SIYAJ3tg0kYfQBhGVgxU0N8xQKIbngEw79gberhEYxeUzbMm3oKQ4AK5TQNSvRW6qSaDWxS5OaTVdYTAAL6V7kBOHmSE2xNBCAAepXgJpSuB2KmcUC4rgcDfZ0GANEcAhBWpkR6sodo7DL6AGLMATEL+5ybk7EVOwGAePhhmVgIil/4r9tJFdECAETaMjCH+iNEyQcQ5skQALFrF6t1jbm5wVRKny+MA4i5ecFJsTU8guEBiEgHhN8Dol0fPR7GlO6ZWOnkDojUehSAaNGmMDFRo9+IUjkgzi4tL8PRde8TIOooUVJSUlJSUlI63dqgA2IYQFjWzIsZle+AmGEMJ0AnXSLdGQUQvR5kMXETAIhduwVH2YH5eAiAmMEBkaeNYQTX5r5E16MKMQ4IDZtuuzdxV6slHRBJRzd6qwaAdtEAQPgFdtoysBMCiGIxGkAIM7oJJYCRDo9g2C3ZMG/aK/BBKhSgTgV7rU6q1aBBmVw+mbNiBECce678OQQgUivJHRAAWjlPN5WdtLc3W3TIkS0kq/hLQemXxUVJG/zibwMAwkEgCtPv/EIBjrGNXnUBLrpo5L5ZHRBrzOOs1YIfMAQg/HGSYQ4IAEsvkLHGoJdtozlWYviWXfIAxKmAk/vQIZzde2i35Wr5EQwfQGwd61VarcIJdyk2ghHpgPAiGFXWI40H6Z6JFeCA2Lt3JMXS/0NvhgMI4Ucw9NFlmSZyFKdyQJxd2rYNjq56J7RyQCgpKSkpKSltpk6DA6Lbhd/4Dfj612dezKg2EMEAafvVjNFC1jQhh0GSUPeuXfIKo/tkMIAQvembUGYdeZHJb0AXpFTXiF+utz9cY9IC0Wx6AKI0fQRj/oo9kEqxXx84IDQr2dQQiHdAiK7sAREVwWinwh0Qdsc8bQ6IYlECCGe9jtauU6eSKOsP0pzTasnkRZADQl87MRWAKJehqc9PNKGk1aRJKfEhFgogQE6ggJEpGJoWX/gPAwhTL80UfykU4Pf4Le790/9gvJnIRgBEaBxgfb2/nbfdJgv8ffvCl2dnCqSd3mBloD9xpZcUQGyRx721EuyA6C7v6a/7tm3S3OAbHMYdED6AcE6ERzCsYpUOhUQAIqyNDXhTgYYcEP5H7Uj8AvonqN4KgT6AMIIBhHJAnKVaXoYVNQVDSUlJSUlJ6Uxo2jjBmPwvwTfdJL+4vv/9p2GdyuUNAYiuXkLvjgIIw5AOiCTL27lTAgj9VPBVu1RvStdIoUCmJ7/jxQEIU2Tpt6UPklcpO+3JJhB+BCPp5ARv1QA4/zkZ2L2bi7QhAGEbOAkBRLEIPXR6WjYUQMT1gGiK8B4QTuv0RTB8AOGu19E6DZqiHLnLh1UZnrhYKMgK9/HH5Y3tNmmjPTWAqKfnJxwQYkoAUQ5iP+MAwjtZ2+149wOMAoiOVp5p3xcKcIolTi1fMnHfLADCj2Ck69ERjHYbbr4Z3vSm6NPJynoRl+EazLN09dIJ4dtCBoMs9toYgLBteOIJ2kt7+uu+vCy3+5FH5EMCAQRbcY+HRzBa1R39xwZK07DzRSrUIwGEZpvyfPXkT3wdiV8AlMvYpMh2wh0Qqc5oD4gRAHGWOyBOR2rwP52Wl2UDFEABCCUlJSUlJaXN1QYjGD/1U9Ix8NKXBnean0ml0sw9IACsbIls87GR2wwDqhiIBJe6SyVYye6gUPts4P19B8QUUzA0K94BkbYMrHSOyDWMcEC0Gg5lmqxUp+8BcdFFwJ497D55pB8x0O1kU0NA8itNA1MvowUBCKsbGsHwC6mmEzEFwzh9EQwfQIh6Hb1jU///2TvzMDnO6ty/X+/rdPfsM9JoJFmyZMmSJVuyLS/ySrzxYN+wXBPM4gSMCVvAQCAXyEICSYAkXK6BOECIDSR2iA0Y2yxeMN5tYcuSZcnapdEymn3rffnuH6e+ru7p7pmq6h5Nz8z5PU893dPdVf3V19U9dd56zzk2YwUoAV2AGBvTLrR3d+sChGabN1MDIhAARkSpAGGLRRGF37Azo6wDolGrL7F/P02yVsDBkgBhN98BA9CPr3IhTjUOiLLpAFLmBYiHH6b3fMc7pt5ezq3tVDSqH4hKgHAaO9iCQc1RMzJJgOjtBTIZjEW682NX+7pzJ92WFyBaIAYqp2BMBDryr62EaGhAKD51CgY5IPTv96JFwF13AW9966QX2myIucLwJqYQIBJRxNCGlkoOiLEx4wfeaWbBOiBYgGAYhmEYZlaoMgUjHAbe9rYaig9AcQqGhYgz4wnAk5mUghHNwIEs4DUWUCfCHfCmx8tfzc+YdI34fLAn6Rxvqk4Y9nRxVfqyFJ3ZF5MYovewh8w7IFatAtDcjEY5mHdAuLJxQzUzALrKHAgAcWew7JzZ0uSAKBds2u0U2I9mAySIFdrhNWSi9ikYYmIMruQ4Ynbjgk2RAwKgOhAqBUMTIMw6IIbQWEaAqGEKxv79us0EFG74DdS2LBQgYsKaAKHWKVfmzspPj3JAOFJxlFRinZig1hfhMO67j4L7rVun3l7OUyYG0wabNej+UQKEHJt03GvHxUhQd0C0t9NTO3fScV9Ua0F7TT9aYIvHyqcjnTiBYd80DggAIhJCREydguHIJpF1FCtcH/iArlsVEvdE4E9WFiDsU6VgdNR3K04WIFiAYBiGYRjmdFKlA2JGqNIBkfEG4M1OEiDGKFixeYxdUs600Ul+uUuINgsOCJFKwYbslA4IRyYxve1bpWDESx0QqUEKgBwR4wH15ZfTFc/2dgBNTYhkB/MOCLdMGBYgAApq4/ZgWReDLZNESrgnlwHIE4kAIxktii4XeMUpBaOWRSjt0TG4UuOIO8wLEEWFKI8cocBXU5fMCBCBADCYK60BYUuQA6ImAkQsViJAmHVARKsUIGrlgAgGSYAAUFq4c4RqFCQ9ITz0EB3X03WxkL7KKRhGBYiGBqp/olq65jl6FADQ59VrQLS10VOvvUai7eTvg0rBAFBaiFJK4ORJDLg64XZPXc5GhEJodw9VFiByOThl2nCdi6Q3gkB6CgEiOU0KBgAcOlR23dlmwQoQcXARSoZhGIZhZoEqHRAzQjCoX+G3IEBIXwC+3ERRz8vUGG3P5jO2PfviygKEyJoXIABgcWN8WgFi2sr72nyUqwGRHiIBwtloPKC+9lrgJz/R8uSbmtCQGcToiASkhFsaq5mhCAS0QpJlHBD2dBJZW+VjLBIBhlNaMFguDSNJKRi1dEA4YmPwpMYQd1hLwQBAKRjJJIkPBQ4IIw4DgA71/kxpCoY9Tg6IqlIwlAABVC1ATKA+BAi7HUh4phYgfn8wbCj9omiAhaKX9ttjNP1IOSBs0fICRK+z1AExMVGafgGQANEP7YnJAsTQEJBKod8+RQcMxebN2JR8BtGjg+Wf19wjOaexAyzlj6AhN0zFV8tgT03hgNiyhR64/35D73W6WZACRGsrIGGjfzgsQDAMwzAMczqpVweEwooA4Q/AiYy+bwBS45oDwmvshNuznASIzNHSS4j2TAo5Ycvn1E+LFuR0NU8jQGQT01+R1CLS5GgCuVzxU5kRij5djcZTMIpoaoIjl6btaAGKMJiyAmiZM7YKKRjZFNL2ynMfiQADySkcEKnap2A442PwpsaQcFXpgACoDoQWMI57Wg0fGsEg0J+O0AYLojtHogYpGF6vnsJUpQAxLutDgACAdGBqAeKxbWG0tk6fflFxgCYFiEBAE7QmCxBHjgCRCAaSdHyFw7Soz9S0AKHZGU6gsyR1o4TbboNbJnH+nrvLP29SgEgHI4hguGKo6khGEYMvv29KOEskQF+at7wFuO++sulVs82CFCCcThIok3YfCxAMwzAMw5xe6tEBUaUAobpAZEf1aCwzQUGF3W/QVr2KbMNje8oIELk0sjYTUZMWBHY1xaYRIJLIOaYJCLT5cMpkvlaDIjtCgb9qC2ga7Yq5c2wQMk7zZUaA8PuBcVlegLBnksjYphYg+uPlomhCJGuXgqEECJvMwZ8ZRaoaAaKbCgwqASJtc5lqgxoIAEMqpWBEb3PoSNagCCWguyAKItZo1LwAMZazJkC4taYu5UIcq9pntmFqAeJXL4Tx1rca0wdFoHIKhtH0I7sdiNkb4EhMOu6PHgWWLMHICM1BMEi3Kg2jkgBxFFp71+efL35SEyCO5Tqnd0CsW4dDbRfifw3cVeQEy2MyzSTbQAJE2RqxUsJZwQGRL9PxrncBAwPAr39t6P1OJ4YECCHEtUKIN4QQ+4UQny3z/GohxHNCiKQQ4lNm1p0t2tqApPCWr9DCMAzDMAwzU8xDB4QSIGJ9+tlyeoLOhI0KEG0rGxCFD/EDxQJENgs4ZAo5hwnBRovcOiNTOyBc2cT0AYE2Hx4kMDjJXa0El2ocEADgSwwiNmQuZQXQrgRXECDKFbwrJBwG+qOVUzBsqdqlYHi9JEAokm7zKRh58UcJEEeOAH19GPe0wB8w2NMTBUUogaKA2pk054DweKieQEUBwqIDIgr6TEaz1gQIIWi+a+mAyIspFQSI3mQYN91kbFP2YOUUHaOSvQAAIABJREFUDDP1TxKuINzJMikY3d0YHaXjRtV7UGkY5QSIhgbgJDqxZ9WNwJ13Fn+gWjrYkZSBFAwAuy75IFbl9iDxm6dKn1QOCJcxhUuGSICITlQWMyrWgAAo16uxEfjRjwy93+lkWgFCCGEHcCeA6wCsAfBOIcSaSS8bAvAxAF+zsO6skC9EyQ4IhmEYhmFOJ/XogAgWXEG2IECoLhDx/lIHhMNv7IR7cZfASXQg01NcAyKdBpxII2fBAdEeik/ZBcOVSyA7ne1buyTuRrJEgFCBv2iozgHRhEEMHDPnGAHIWTCSqyxAZKZJweidqJyCobpo1MIBYbMBCacuOqTcxucrGKT187FvMEjzpjkgRpzGC1Cq1fNFFVUhSinhSkUxgYDh4Fx1IamlAOH1Amm4MBxeit3ZMy2LPz7f1F0wzAoQonFqB8QIwujqMratvABRxgFh5rcn5W6AJ1VGgNAcEIWCwVQOCLudjolfbvgsHQ/f/a7+pOaAOBg3JkCM/ME7MIIQ0nf+a+mTmgAhDaaZIBKBExnE+sukR2lzV7EGBED/X97+duBnP6vYane2MOKAOB/AfinlQSllCsB/Abix8AVSyj4p5UsAJieZTLvubNHWBoznWIBgGIZhGOY0U+8OCAsRpyNcRoCI0gm3I2DshHvxYroynR0aKXo8lSIBImvBAdHWEMP4eGXDq0smkDPhgJjUOEE/sQ/OjgARCAAjWU2AmGT7pvSSqYtQDiYrOyDsmQQytspdNMyS9jaUvT8dNhtdyC0Sf7q7yQHR348hR6spASIQKNPVIR6HgETC5je1v7UWIOx20rv+4U/24RvpP7Us/vgqhDhWBQhHs+aAGCn+bqq/RxEy/BWwN9Axl4uWtuHMuY3vcMrbAFc2oe/U2BiNRxMgCms2TOWAAEis2OG7ELjsMuDrX9d/o0+cAMJhnBr3GRIg2pb5cA/eDf8jP0GJWpmvc2FMkLU10fGTOFmmE4YmGBYKECp1qOi37l3vogPhZz8z9J6nCyNfsUUAegr+PqY9ZgTD6wohbhNCbBNCbOufXABkBmhrA8YzLEAwDMMwDHOaSacp0qhVZFcLqkzBcGopCMlBPRpTXSOcAWMn3A0NwIQ9hMmFFlIpwIUUpMO8A6LFT2fj5dIwMhkSFXLT2b6ncECIqOY8MBMBF9LcDIAEiMHj1gSIoXSQWlIWRh5SwpFLITtFwbtIRK83UF6ASBrOVzdCoeiQ9ZkTbEoEiKVL8w6IQVGFA0IJENr+JxzmPkfVvbaIKgQIgF43OOqAhKjKAVFLASIQcdKxUsYBkXL5kYHTsADhDNFOpYdLUzDM1D/J+rTjSX0AWgcMLFmC0dFiAWIqBwRAAsToKIDPfhY4dgz44Q+Bu+8G7r0XWLYMo6MwJEB0dAB34TbY0ilatxBVnMFonYtmOn5SfWUEiLwDwp+vu2G3k9Cyb1/B6y6+GFiypO7SMIz85yuXVFUmGaW6daWUd0kpN0kpN7VUOjpqiHJAZCdYgGAYhmEY5jSSStVX+gVQtQDhitD6qSE9kM1GNQEiaHx7aW8Izmh5B0TObiJq0iK3Zh+d55UTIBIJEiCmtURPUQPCHqtSgNCC1CYMYqTX/Hz5/cCgVvG/KBLOZGCDnLLAZpEAUSYFw5FJTFlDwixZv3UBoqkJxe4TJUD09aFPmhcgSmpAaAJE0mGwl2fBtqZzQEhpXoAYGNDvW2G6GhBmf37CYSrcKcsIEAk3RfpGPwN3mMTBzFh1KRg5v3YMqeqkSoDo7i5JwZjOAREOa2aOa64BNmwAbrsNeO97geXLkf3uv2NiwpgA0dkJvIZ1SHoagN27i59UdS4MOiCcrXT8ZKYQIFKO4gNk61bgyScLzFA2G/DOdwJPPFFQxXX2MSJAHANQmNWzGEBpeeLarzujtLeTbaXo4GcYhmEYhplpUqn6Sr8Aqq4B4Wmm6CM9XCBAxOiKnytoPIhN+cLwJEsdEE6kIZ0moibNAdHoreyASCY1AcKgA8JbToCITyApPIDDYXxshTgcyATDlgWIQECrAQEUR8IGWv6Fw3rBw5LL+FLCkU0aDpaMkAs0lL1vhLIpGPE4EI3iVM6cAFE2BUMTYJIuc0JSQ0OZuG6SAKFi69MpQNTaAREK0ZxlB0oFiKgrDL/fRIdcv0B0cgyWMF+AVQYntUc5coRuyzgg1qyhr+gZZ5TfVt4BIQTwla8AZ54JfP/7wHPPYWzZOfnXTEckQj8XQ4FufTyKvAPC2HfK3U7HT27QnABx7Nikt/7kJ4GeHr2aax1gRIB4CcBKIcQyIYQLwM0Afm5w+9WsO6OoIpQ5dkAwDMMwDHM6SafnnQPC20LrZ0YKUjDi5gPqjD8EX7pYgEinLaRgaJFb2FXZAZFMUloFPMbacIa9pSkYzsQ4Yg6L9R80cpEmNGMA4/00X66Q8Tz4QKDAxVAoIqiCd1OINpEIkIQbWbe31FqvHBRGC+YZoFB0MNM2E6jggNA4njZXAyIYBFJwI+Py6hvVxJuU07wAMbk1K7q7KZDtoLaySgg43QJELYtQhsMVBIjRUUQdYVMlUHw+isGyY9WlYIiQdjypL+XRo7Rj7e0lDogrr6TfgEqFMkOFmV/XXgu8/jpw662AzZZ/3IgAoT72XvcUAoTB31dPh+agGapcAyLtLBUgAHJB5Gltzad61QvTChBSygyAjwD4FYDdAO6TUu4SQtwuhLgdAIQQ7UKIYwA+CeDzQohjQoiGSuvO1M6YId8FI8oCBMMwDMMwp5F6dED4C6znFgQIXysFbqotJQDIuOaAaDBhqw6G4M9NUIEGjWocEA3O6R0Q0+6v9nzEW+qAcCUnkDQZtE5GNDWhCYMYH6AgzB0y54AYR5kUDAMt/+gCvUA82Fo6Qfm2iLVzQBR2CslfvTZIWQeExvGU+RQMAEh4IiUOiLTLXApGWQfEjTcCO3bko93ZEiDKOSCs1r9VDoiSYHhkBGO2sKmL66rVaNFF4EQCGdjh8Bh3Eg0tPRcZ2IFf/5oeOHoU6OpCDrYSBwRAx1AlQqWlZ/KYESAASsM4gjIChLLCGPxO+TortD4F8h/u5JShtWtpP3/3O2NjnS0MVT+SUj4spTxTSnmGlPLvtMe+I6X8jna/V0q5WErZIKUMa/fHKq1bD7S1AXF4IRIsQDAMwzAMM8M8+CDwhS/Q/Xp0QDid+omxBQHC30onwnK8QIBQtmqviSBWneUXRHWqCKWpqEkTIJzpGEIhlG3FmUxIeJGAmG5/HQ7AZkPIkyzpguFOjSPpqs4BYWslASI2aF6ACIUqCBAq0pxWgACi/tbSCVJXa2uYguEOupAQtG9m25Y2NZFJQe1WoQBxzKQAoV4bKxQgNAdE2m1OTAqFyggQdjtw9tn5P60IEOo4q7cUDDFSKkCMCnMOCL9fc6FHiwWIBDxGY3MAgL21CY/hKsj77qOiB1oLzokJ+tOoYADoAoQsU6nQigCxL9VNKxaqGtp3yqjLw9MWQg4C9rHKAkTGVXyA2GzApZfOEwFiPtLaSge/PVWhLxPDMAzDMEyteP55yi0eHq5PBwSgXxq2IEAEQnbE4C0SIJAwZzkGANFIV/2yQ/qJu3JAwEIXDMTjaGsr74BITVA0aygg8HgQcpc6IDyZcaTdVQoQLSRAxIfNCxCRyNQOiKlqOKgrxKOeMhNk0i5uBL8fGEcDchCwBc05DdTV67wAFA7nd6AP5lIwHA7arQlXY9UChHJAlAtcFVYEiFzO3DqT8XrL1hWtOgWjJBgeGcGwtJaCUThAGU8gDq8pXTYYBO7DOyAOHgReeYUcB1oLTjVmo4TD9DujTAqFqO0ZFSA6OoBdE5pAVuCCUF2BxHQpXxrCbsMoQrCPG0/BACgNY/9+6iBaryxYAcLlAqTHB2c6rn/LGYZhGIZhZoIbbgCyWeBXv6pPBwSgXxr2Gq9BoHA4qBaBiBYKEOYsxwDgbKKz/IljeieMfAqGmTlzOmlQsRhay2QYAEBqzETeuduNoKucADGBjKf6FIxmMYj0BI3HEzYe9IfDUwsQU829y0XB4LCzrdQBkc/Jr50Dwu8HRmQDxhGEx1uuUV5lVF3HcmkY/TDngAAoeB13lKZgZD3mUzCy2fL1FhRWBIhy983Q0gL095eGOKoDsDA3/XkHhCMR1VUMKYGREQxlQ6YFiCj8RRYNGYubdkAEg8BPcROk3Q78+MfA8eP5ApSAOQFCiQvl0jDMbq+zE9gdLxUgVFFeM46wMXsEronKAsRkBwSg14GoZxfEghUgAMAe1D60cnIXwzAMwzBMrbjgAoqiHnqofh0QKoqzeNU7bgvAFivtxGBme65WTYA4XuyAcCEFYXbOtEp8lRwQ6XETlfc9HgScxUUopQT8uXFkTLaULKGpCUE5jgaQl9/XWDsBQrinFm0iEaDfpqVgFEar2vo2g1drjeDzAWPQBAiTh5gSICYXopROJ8bQYFqACASAUVukpAhl1mveAQFUrh8AmBcgCvU/qwLE0qWkE5w8Wfx4Om3tp0cJEACKRZtsFgMZ8zUgYvBBxHUBIhejFAyzDoghNCG65Wrge9+j41drwanGbBQjAoTpGhBAeQHCRKePcUcErlgZAeLIEcTtfiTcpYPasIHmhgWIOsUZ0r7V5ZKkGIZhGIZhaoXdDlx3HfDII3Thox4dEOoypsWxxe0B2BO6ACFSCWRhM9Wi0tNGlxnjJ0sdEJgmmC7B651SgMhojgO735gDwm9PFNUhSCSAACaQ81XngFDRdafWqd5sCkbZLhhqkNNcUo5EgFOyjYp+juhznhcgTARL0+H3WxcgVApGkQPi6qsxdv7VAIQlB8QIChwQP/sZTrkWWxYgSupAFFCNA8KCGQmA3iTk8OHix60KEOEwMIJJRRG146UvZa0GhD2up2AoAcKsAwIA+ra+XT92LaZg1FqA6EMrFYA9ejT/eNZkCgYARJ0ReONlBIg9e3A8sAoOV2ko73AAF1/MAkTd4o6wAMEwDMMwzGnihhsognrhhfp1QHg85v3ZGglnAI4CAcKWTCAlzEWavk46y0+c0iMB1YZTuCw4IGIxtLXppTcKyURVkG0gIPB44LPR69VF82gUCGIc0l+lA0JrkbcIxwGYC1AaGoAsHEg7vOUdENNsKxIBTmRa6Y9ClcZKAdFp8PuBF3ABnsOW2jggPvIRvPrlhwHAkgAxJCPkfHj0UeCpp3B322fg8pgLjcrUTC1hNlIwphIgrOiLPh8wZpvkgNAi/VNJ8zUgovDDlixwQMTNOyBUEdWD62/SRc6CFAwrDohCDU4xOko/i0bH1tEBSNgwHFyC3b88gve8hw6znOaAsBv5vdGIeSLwJcsLED2+1RXHtHUrsGuX3k2l3ljQAoSnkQUIhmEYhmFOE9dcQ06I0dH6dEAoAcIiKWcArmSBAyKdRMpmLoANLqJIINVfWoTStABR4IAAKCe+EFMOCI8HXhu9Xl2Fj05IBDBhPvqdjBZdL8JxJOA2JQA5HBRMJ1zBfBoBAFMCxNGkNkGFAkTSfLA0HX4/8Dn8Pf4E3zd1pRuo4ICAvstWUjD6s9pGP/UpoL0d9wbfb/pracYB4TdYXqIWAoRqElIrB4QQQDqgRfwqSlcOiLS1FAxHqiD+ipt3QHR20u2RiSbgqqvojyqKUAKVHRBmxAw1rpcHuzG68wjuuQd49lkSWVJwwuk2Hn4nvBH405NUkVgMOHIEh72rK36Wqg7EU08ZH/fpZEELEL5m+lYnhliAYBiGYRhmholEgIsuovv16IAIBqsSINLuAFxpPQi2pxJI281tL7SEzvSzg6UpGMJsdFjggABK0zBUVXqHwRQMDygoV0HwcG8STmRgj1RfAwIgAcKsYwSgwypuDxQ5IGTCmAARDgOHY5oDoqAQpXKHOAK1TcFQmD3MAgH6ykxug2pVgAgGgf6MFlC/+irw6U9jPGOuCwNQvykYXi/Q1lY7AQIAsg3lHRAjMOeAcDiApM0HZ6qgC0bCvANCBfrHjwP4y7+kNsc+nyUHRHs73ZbrHGFWgIhEgK9/HVh8UTfObaIaEIcOATIaRxJuU/uY9EYQzA4Xt1nZuxcAcNhdWYDYvJm+Y/WahrGgBYhAK33Dh4+zAMEwDMMwzGnghhvoth4dEB/+MPDVr1pePeMJwJMpSMHIJE0LEJEWBybgR26ktAilzV2dA2KyAKEcEIaCbI8HLlnsgDiyiwL+cFdtBIhOnEDKZj7gD4eBqAgWCRCq4J3dO30Ryv3jpROkOoQ4/LUtQqkwK0AIQS6IWjogepNaQN3SAnzwg0ilTDVsATAzRSjV6zwewFZFpLZ0aakAUU39WxmujQABACmnH850TA+sE+YdEC4XfXTHjwPYsgX4m7/JD8vjMfdZNjWRyLBvX+lzZgUIAPjkJ4Gzru2Ga7AXQWcCBw8Crp2/xx5UTpsoRzoQgUumitus7NkDADjgrCxAuFw0JSxA1CGhdpIVR0+yAMEwDMMw8xkhRJcQ4gkhxG4hxC4hxMdnZSD1LECcfz5wyy2WV896A/BmdQHCkU4gYzcX0Xk8wBhCEAUR3fAwOSCcfpNzFggAY2PTOyCMCBBuN1y5YgHi2G7a15altUnBcCKDlEnBBijohFEoQMSp4MV0NRwiEeBotBHSZityQKTGScBwBmqbgqGwYrRpaqqdABEMAj1Jzflxxx2A349k0vzX0owDwug+KwHCavqFopwAUY0DwtZUWYAwk4IBUPtIm8wVVXSNw7wDZdGiUtfCyIi59AuABK4VK4D9+0ufsyJAAACWLAEAXLioByf2ReHd8Twew1Wm5r/EdQKQACEEDjlWTrmtrVuB7dunFsdmiwUtQIQ76Zs9dmqK5r0MwzAMw8wHMgDukFKeBeBCAB8WQqw57aNYu5bOdFVVvXlEzheAL1eQgpFJIuMwH2mOO8KwjespGCdPkgPCHTAZOTU2AsPDaC1TYxEAZJwEBVeDMQeEI1ucgjH6OhWNdHe1mhvXZHw+pLV5ylgQICIRYFQWCxD5ApsGBIgc7JDNLcUOCCVABOsjBQOgj3NyCkZUc/FbESCeip0Hef8DwCc+AUBz2syQAOHzGS/tUUsB4siR4u6q1QgQ/ogLMeErESBGETLtgMi6tZ3TPkCRiJt2QACUhnH8ePFjVgWDlStr54AAkC/EcV7zEYR3PgWRyeBxXGnqGMuFKggQy5YhmvVM+Vledhl99s88Y2HsM8yCFiAii+jgn+hjBwTDMAzDzGeklCellC9r98cB7Aaw6LQPRAiqDPa1r532t55xAgF4kYBMZwAAjmwCWZMOCACIOUNwxvTLdidPAi5hoQilFrEGAmTHnhy8KgHCUJDtdsOWSsDl0rfjfOM1urN2rblxlSEZIEEqbUGwCYeBkWyFFIxpikiqTgLpSGtxDQgtPcUVrH8HhMNhXjgIBIBsTiBx7U35lZNJ8ykYLpfm2jEgQBillgJEOk3fH0U1AkQoBIzaClqXjo4i4/YhDZd5AcKjHQyaPUQJEFYcEJMFCCsOCIB04cOHS7vlVCtArPEfwZnHHkPO6cLTuMTcPmpf0HTfJAFi9eppP8sLLqDn6zENY0ELEI2L6ZsdG2ABgmEYhmEWCkKIpQA2Anhh0uO3CSG2CSG29U9umVBL2tstntHWOdpl6NQwXdV0ZJPIOs1HmklPCO54sQDhhIX+gY2NFD1kswiFSlvs5bRCjYaCbI8HIpEoCoIbT+5CwhnIW62rId1AAoSV+QqHgeG0tRoQKlBLhNqKHBDpCVrf3VBfAkS5IpSBgPnOsSpgLmwcYsUBAZALol4FCKA4DaMaASIc1lqXFjgg0j46gMymYOQ8BZ0IEwm4hk/hOBZZEiD6+opFg+FhawLEypXkGJictmJZgFi8GLDZcIbjCC5OPIahM7cgDp+p+ReNJEAkTmpznssBb7wBrF49bT0Pn4+KUT75pIWxzzALWoBwR+jgHznBAgTDMAzDLASEEAEA/wPgz6SURWGDlPIuKeUmKeWmlpaW2RngHMbWQAJE9BRFda5cAjmn+QA25Q3DmypIwTgh4ZQWIifVu3FkBOFwmVzouIk2nN3dwLFjWBSJYXCQAs5l8V0Yal9jPvotQzZsXYCIRIDBdBCyCgdEzD/JAaGtbyg9xSDVFKEEKhehtNIFVQkQasqkrE6AmK4IpRUBwmoHDEWtBYhQCDiVa4Hct48mbGQECS9F+mYdENJXkIJx4ACElNiLM007UBZpHrbeXv2xgwf1fTfDihV0W1gHIpOhY8ySAOF0Ap2dWDb8MjZgO/YsolahZo4xezN9QVOnNAHi6FEgkcg7IKbb1tatwLZteqpSvbCgBQj1DT+xnwUIhmEYhpnvCCGcIPHhR1LK+2d7PPMNW4iikFifLkBkXeYjzUwgBF9aj+j6T1JKhyUHBAAMDZV1QKgUDEPR8KZNQDaLza5XMThIueJrsQupM882N6YKyKZmAEDOwnzli1BOTOS7CuQSdEl4usKdSoAY8xU7ILJaCoYnVF8OiERCL+oI0C4XbtcoSrRQAkQ6TbdmA2Cgfh0QWgZAiQBhtf5tKAT8GH8E8dprwK9+BYyMIO62JkDAV5CCobWV3IszLTkgAD0NY3CQXDIrV5ocD8oLEOpzteKoAAB0d6P91V/CBomnXVcCMCcAOVompWBoHTCMpGAAVAcikwGef97swGeWhS1AaL9+8aFYUX4UwzAMwzDzCyGEAPA9ALullP802+OZjzgjFNXFB0iAcOaSlgJqGQwhmCMBIpEAoiOav9qqA2JoqKwDIj1uUoAAcK7chsFB4Mi2frShD+6N1dd/AABbMzkgci7zl72VACGkzEfnuXgSKTjh8kx9qq8EiBFnK10m1S6VKgeFJ2jxcnkZalGEEihOw6jWAaFSMJSF30pwHgrVpwDh9QJtbbVNwbgb70Gmcwnw138NDA8j6rAmQAh/QQqGJkDsw0pLRSgBXYBQRSTPPNPcdgBq6dnQUFyIUv1mWM6Y6+6GyGYxjgAeGTwfgLljzNVCb5wdtCZAXHQRtXKttzoQC1uAsNmQdXvhRxRPPz3bg2EYhmEYZga5GMC7AVwphNiuLdfP9qDmE0qASA2MI5MBPEgALguXlMNheJBEZiKB3l6t/gNQlQBRzgGhCi0aioY7O4H2dqyNvoShIWD8+V30FpfWRoBwtJEAIS1E5pGI5oAA8pf0c4kkknBPG+woAWLArvUq1dIwZDyBBNzw+atPL1G43RQMqftmUY1jCtMwapWCUY0AUa8OCKC0FWe1KRhpuNB76+fokvqrr2LCEYbHY36btkBBCsbevYiF2jGOhqodEEo8sOKAKNeKsxYCBAA869iK3ftpkszsYyBkxwhCsB8+QA/s2UO/a83Nhj7LhgZg48b6qwOxsAUIALYlXVhpP4innprtkTAMwzAMM1NIKZ+WUgop5Xop5QZteXi2xzWfcC2iuhnJo6eoowCSkG7zAbW9kc72R4+O6gUogapSMMo5IHKxBDKwUxuF6RAC2LwZy4fJAYFdJEC4z62NAOHqoOhaWBAgwmFgAsU5BTKRRAquaQN9j4em9RSKBQh/zx4cQXdNgmCFEOSCcDoBu938+rV0QExOwUiS4WNGUjCi0dkTIJYtKxYgpitcOBUqDeHQ5bdS5J/JYEyYb8EJACJQnIIx2kqWBbPz39RE6ygBYu9eErmWLzc/JqC0FWetBIjX2q6CqmtsZv4DAeCHuAXtj/4IeOCBfAcMCGFYTNq6lfQidYzXAwtegBDr12GTawcLEAzDMAzDMFXQfdlSAMD4joNIJDQHhMd8ROdoorP98WMkQLhQfQpGOQeEPTaGpMNEAYFNm9A6tAfu9Djc+3dhwt6gX4KtEu9iEiCWr62iBgRQIECkDDkghCAXxMlsKz1w6hQgJVoPPItncHHVhRAn4/NZS78AZsYBUYsUjHp3QBw5Qs0TgOodEAAwHHMDf/7nAIARhE13wAAAR0NxCsZQMwkQZudfCDInnThBf+/bR/tstc6FasWpaoJULUBs3gz4/Ti05s35h0w5IALAHfg6BpdvBt77XuCVV0iAgPHP8kMfohQMIzrr6WLBCxBYvx6d8YPY/2p0ygq2DMMwDMMwTGUiXQH021qR20cChBtJwIIDwt1Gl1onjo1U54BQl2w1B0Q8XtyurzF6DCOBxca3t2kThJTYiFfQMbwLp5rX1qQDBoB8dO1vrE0KBpKUgmHkinIkAhxLFzgg9u2DNzqIZ3FRTR0QADkgrAoQM1EDYnIKhlUHxOhovv5nCWYFCI+HDqtaiD9Ll1Kgqmrd1UKAGB0F8P73A1dfjVeCWy05IJQAkTt2Aujrw2CTNQcEQBpgoQPCSv0HxcqVQDaru0aqFiDOPRcYH4dnvT4osw6IFNz41W3/Q5MzNgasXp3v2mJkWytXAuefb811NFOwALFuHQQkzpK78Nxzsz0YhmEYhmGYuctgw3J4eg/lHRDCggPC00Zn+/FeckB4hEUHhMNB0aHmgACK0zBaUz0YD3UZ39555wEANuMlrMUuRJfWpgMGAP3yvsUUjEoChBHNJhIBjsS0trOnTgHPPgsAeA4XWQoIp8LvtxZkArV1QKh1lANC2dOtFqHMZkngKodZAUII4B/+AXjXu8yPZTKTW3FWW4QS0JxEXi/wm9/gSe+1lgQIZ5icR7mXtwMA+sLWHBAAOSCOHycBaN8+a/UfFJM7YVQtQACAEEUpIWb2URVuPeXqAu69lw6kLVuQzZrfVj3BAsS6dQCADWIHF6JkGIZhGIapgmTnMrRFD2JsKAM7chBe8wG1v5PO9pN9JEC0N1ksQgnQZXPNAQHoAUU6DSzK9SDWZEKAaGtDorULN+AhNGMQtnW1qf8AoCoBIhAAYrbqBIi+MQ9FWadOAc88g5g7jCPe1TUzeCiqcUB4vbQoAUJK6wKE202HU62KUAJ6GoaUxSkZZgUIAPj0p4ENG8yPZTKozFBCAAAgAElEQVS1FCDKiXjj4xZacAJwB13IwgbxKgkQp0LWBQjlgOjtpeOhGgFCravqQNREgADV4lBYESAmJgBceSUN6JJL8ikiVj/L2YYFiOXLAZ8PV7bs5DoQDMMwDMMwVeBctRxLcBSvPU+Xlm1e85e7g12kFqT7KQWjo9liCgZAAsTwcPHVWwAjp5JoQx9SbSYECACp9ZtxJZ4AADRsqaEA0aI5ECzkPAgBiIbJOQXGilACdGV7eBhAayulYDz7LPY3b0G4sfZhQjUCBJDXkwBQi9ZczpoAAVDgXKsilIAuOvzylzSVx49TwJ9O16aegxWWLKHbQgHC6lVzl4s+u0IBYmwMlmpA+PwCMfhgP3oIsNlwyr8cDofeJcUMixaRyLNtG/1dTQpGSwsdF4UOCFWotRoKHRBmRAOXixatO26+kAMLEHMdmw04+2yc596JF16orwqhDMMwDMMwc4nIpuVwIIujj9MlRJvPfLQZWkKXG7ND5IDoaLKYggHkI9bJV28n9hwDAOQWmxMgxPmb8vfbr6qhABEOA/fcA9xyi6XVHZFiAUKkjRWhBMgBMTwMoK2Nquy//jp2BC/OmzJqyQUX0GKVpibdAaHSJ6wKEIFA7YpQAroA8fLLFE9s26anZcyWAOH1Au3tekBdjQMCoMO0sJirVQeE3w9EoV3eX7oUsayxY7Ucqg7sb39Lt9U4IIQo7oQxOlq9+wHIN8MAYP4YCwR0oUzBAsR8YP16LBnZgWRS4ve/n+3BMAzDMAzDzE1azyevcfz3uwEANr95AcLVGEAOArlhEiBaI1U6IApSMFTwFN9HAoSt25wA4b2EBIgRWwSuJe3mxzMVt9wCdHRYWtUZKS5qIFLmUjBGRwHZ0gq8+ioA4AX7RTMiQPzd3wHf/a719QsdENUKEIUOiFoIEErcOnqUbnfupCvzwOwJEAAJPk88Qakh1QoQoVBtUjB8PiAGbVLOPBOplPXaIEqAeOIJ2rfCYN8KK1aQYLN9O5VDqYUA4fFQrQohzBeDbGwsrnsCsAAxP1i3Du7xQbSjl9MwGIZhGIZhLGJfSV7jhmOvAwAcPgtRhc2GMRECRkfQ1we0RWrvgEgf6gEAuJeb6IIBwHEBFaI81lDDDhg1INxoQ8zmz0fUtpS5Lhi5HJBq1Dph2O14Nr15RgSIaqmlA6JWKRjq2FIOiCNH6LZQgPCb6PZaa667jsa0Z09tHRDZLKUGWErBmCRAJJPW0xw6O+l2+3bgjDOq7/awciUJEBs3kpj02c9Wtz3F8uXW9rG1FejvL36MBYj5gFaI8vourgPBMAzDMAxjmcWLkREOrJLkgLBbcEAAQNQRQnpgFFICzaEaFKEMUY/EvH38KAkQnpXmHBBobMTulksxseVN5scyg4TDQEzoXm1bmmpAGHVAAEA80Ep3zjkHPcOBuhQgCq8G12sKRjkBYjYdENddR7ePPFK9ANHerre8VHNn1QGRT8Go0gGhBAgpq6v/oHjTm0iE+NKX6LO89dbqtwlQIUorc9/SUipApKrQZOsBh5EXCSGuBfANAHYA35VS/v2k54X2/PUAYgDeJ6V8WXvuMIBxAFkAGSnlJtQbmgBxbccO3PbMHyCXs1YEhWEYhmEYZkFjt2OiqRtnDZAA4fBbiyrirhDkCNkV8gKE1RSMbBZBjEOIhrwDwn6iB0OIILzI/KXps/p+Z34cM0w4DIwhiGZVAyJjvAaESk8Z97UhDEBuuQhDr6IuBYimJkrBUB0wgOocEL29dL9WAoSUFLQKAezdq9XWwOwKEEuWAGvWAA8+SH9XE7SuXElFNnM53T1itQZEf6ED4hnrDgivV0/Nqab+g+Kyy+izqzW3326ts0lLC/D888WPzXsHhBDCDuBOANcBWAPgnUKINZNedh2AldpyG4BvT3r+CinlhroUHwCguRno6MC5zp0YGSHFkmEYhmEYhjGPXLocZ+AAAMARsOaASHjCCGTIrtAUrOJyn3Z53zYyhIYG3QHh7utBD7ryV//nOpEIMJrTcwrsmSRSwm3Ijq7mYMRFDoj4xouQzdanANHYCGQytJu1cEDUIgVDBeBjY8DAABWe3LKFUhRefpmem00BAiAXhHJ5VytAJJNAT4/u+KhFCkY1DghArwNRCwfETHHRRcAnP2l+vZYWOq5yOf2xdBWabD1g5Dr/+QD2SykPSilTAP4LwI2TXnMjgLsl8TyAsBDCWhWd2WLdOiwZ3QmvF/irv5rtwTAMwzAMw8xNfGcvhx10tuwKWosq0r4QQiC7QmOwSgcEkK8DoRwQ/qEeHLd1VdUSsp4Ih4ExGURuVBcgMjZjc68EiMPLrwRuvx2nNt0AoD4FCDWmoaHaOCBqkYLhdtMyOqqnX9xAU4gXXqDbehAgslm6X40AsWIF3e7bV50DQqVgZBxuoKurqhoQgC5A1MIBUW+0tNBnV9h9ZN47IAAsAtBT8Pcx7TGjr5EAfi2E+L0Q4rZKbyKEuE0IsU0Isa1/cqLL6WD9ejj3vY6/+WIGP/0p8NOfnv4hMAzDMAzDzHXcq5fl7zuD1iL8rF8XIMK+KotQAsDwcFEBvYaxHvS7TdZ/qGPCYWAcQcjdu4GbbkIg1oeM3ZwA0ZeOAN/+NgZSdEm7HgUIFQDfd1/9dMEAqBDl2JjeAePqq+lwrRcB4pJL9EKY1TogACrSWK0A8RQuxd5z3gHYbFU7IFQdiHp2QFilVSvNUhgeLwQBolyJX2niNRdLKc8FpWl8WAixtdybSCnvklJuklJuamlpMTCsGrNuHZBM4uPX78P69cBHP1rac5VhGIZhGIaZhuXL83etChC5UBhhjKCpCXCktEp+tXJAxGIIJIcwHJg/AkQkAuzGWbANDwJ792L70v+Fe/1/bHhdQK9XoIo81qMAcemlwB/+IfDFLwIvvkiPVZOCkUrRUk0KBkBpCGNjugPijDOAs84CDh6kv2dbgHC7gauuovvVOg08HnJAVJuC8f/wUdx/090AULUD4oILgFWrdCFiPqHC4oUmQBwDUPgLvRjACaOvkVKq2z4AD4BSOuoPrRClc89O/Ou/UoXXL3xhlsfEMAzDMAwz1ygQIKymYNjC5IDoaJfAQw8BHR26z9oMBQJE3gFx7BgAYCw0fwSIcBj4DP4RL/42Drz+Ou687D684T/X0LqBALUunAsChBDAt79NV91/8AN6zGpwr67cT0xQjj1gPQguFCD8fjrstNCiqjHWEtUNo5qg1WYjF0q1KRgOB811NEp/V+uAuO02ajNaR51xa4YSIPr69McWggDxEoCVQohlQggXgJsB/HzSa34O4D2CuBDAqJTypBDCL4QIAoAQwg/gDwC8VsPx146zzqJf3x07cOGFwIc+BHzzm8C2bcDhw8A//ROprtdcA/z2t7M9WIZhGIZhmDplmZ6CYfNZc0DYG0OwI4dzwwdJgHj3uylqMYu6vF/ogOihrOF48/wSIACB4QmKSMwEdELQ+pMFCKXd1ButrcC3vkX3/X7rnetU4Pz663TOf8011FHBCoUCRHc3zWm9CRBvfjPt89Kl1W1nxYrqUzAArRClZm6q1gExnynngJjrbTin/cpKKTMAPgLgVwB2A7hPSrlLCHG7EOJ27WUPAzgIYD+AfwPwp9rjbQCeFkK8CuBFAA9JKX9Z432oDR4PJQ5pLTC+/GX6gbviCvo/escd9EXbuZMeu/xy4IknqN0OwzAMwzAMoxGJUFI8YPmypqOFekPePPwtqsD23vdaG4vXS0uhA0ITIJKt80eAmJxGkUqZC+giEb0+xuAgBdD13CHk7W8HbrmlumBapW584APUYeA737G+rYYGvQhldzc9dvbZ+vNWhY1asngxFe+88srqtrNyJXDgQEE9FQspGECxAFGtA2I+Mx9TMAxJyVLKh0EiQ+Fj3ym4LwF8uMx6BwGcU+UYTx/r1+cTykIh4PvfB776VeDaa4G3vpXyueJx4N/+Dfj7v6cvcGMjsHo1GSjWrCGXxLnnwlDbI4ZhGIZhmHmHEJSG8corsNpmwt1CAsblB74HbN5MJ1lWaWwkB0QHBYnyaA8EANlpIaWjTiEHhB4Umr2iHIkUOyDC4fo/l/2P/9CvBFtBXbnfswf453+uTsxQRSjHx6keAaA7IJzO+gkUrZiIJrNyJc3766/TflkVDvz+YgGCHRDlcbtJ5CknQMzVOavBYTiPWLcOuPde+vUIBnHddXq+lMLrBT72MVJLf/xj4KWXgN27gQcfBL73PXpNKEQOiS1bKGWxtZWWxkZ6rqGh/n/UGYZhGIZhLKMECIvRia+DBAhvchR43/uqG4smQITPoivd6YM9GEELgi3zpAcnSgUIs1eUJwsQ9Vj/YTI2m2V9C4AuQJx/PhWfr4aGBuDUKbpQqRwQXV26EWg+oTqRvPyy9fQLQGvFqdWA4BSMqWlpWYAOiAWDkip37AAuvnjKl3q9wJ/8CS2K3l6qD/HYY8DjjwM/+1nl9QMB+rEKBOjL6/fTPwqlkqrF4aAlEgGWLKGlq4v+0fj9+sKCBsMwDMMwdcPy5eSEsChAdJxFEbV0uSBuvrm6sSgHhBYMJvf3oAdddZ1iYBaPhxarKRjhMNU8A+aOAFEtZ59NdRH+4R+qP49uaCDxAdAFCCHoPVQnjPmCasW5d6++r1ZQKRjj4ySccQpGZViAmM9cdBF9kg88MK0AUY72duDmm2kByIrV10cHzKlTlHc1Nkb2v9FR+sKpZWKCbtNpWlIpSnnMZOjvwUH9h20yQgDNzfT+7e0kVtjtpAzbbHp13/5+GoPHQ4psKESv7erSxY1IhFRI1ZbI46HHwmG6bWigRR3wmQz9sxsaIqGku7s29i6GYRiGYeYwH/0o5aRaPCmwN5JaIN7yluqrIUYiwIEDeZeA7OlBD1bMKwECgF7jAnQuV40Dor299uOrN8JhcjDXgsI6CEuW6Pff9jZyCswnOjvpQmw8Xr0DYmSE0twnJoB3vrN2Y5xvtLToLV4BFiDmF83NJIXecw/wla9U/amqYF1ZlapBSvqHcPQo1U4aGyPbUjSqCx29vcDJk/SaXI6WbJa+4C0tpMIqgUGJIIcPA08/rf/TMYrHQ8q66gGscDjoosfKlbTvUtLicJDBZMsW4LzzyLVhhIMHKc1lwwaqETof2+swDMMwzLyjq0u/ImOFpUuBq64CPv3p6sfS2Ai89JJeF7P/GHpwBbrC1W+6nigUIFIpc8UBVRFKdb65du3MjHG+UjjXha6AP/uz0z+WmUa14ty5szoBwu8n1zgA/Pu/U5F/pjytrdSZUcECxHzj1lvJAfHII8Bb3jLbo8mjXA7NzXRBodaMj5OwoSxQbjcJDIkEiRMjI3Q7NqYvyST9w2pqoiWRoL7AalGODSHouR/9iP6226mzSCZDjycS9MO9bh2JJGedRT9qDz1EhYEUXV3A1VeTOaW9nUSV5mb6AROCFrudxsRCBcMwDMPMYTwe4NFHa7MtVQMiDAQwDnd8FD3owvp55oAodDFY6YKRTpMlfqGkYNQSJW45HFT/bb5TCwFCtSb9m7+pvszLfKelhdzsUlKMM9fbcLIAMZlrryWZ6Qc/qCsBYqYJBqsrMG2EgQHghReA556j/sFuN51fuN303GuvAb/+Nf0DdLmAyy4DPvhByox55RU6D/npT0klnYrVq6lb1y23UMshhmEYhmEWMI2NQDyOsDuOLlALzvlWAwIgB0RfH9230gUDoJThiQkWIMyiHBBdXQujLpuqA2G1BScAvP/9dFH1U5+qzZjmMy0tFB+NjtL3nB0Q8w2nE3j3u4FvfIOKJqjmq0zVNDcDN9xASyVSKUq7WLxY788MUIXiD36QUkqOHKGPRi3xOCmCAN3/2c+Az30O+Iu/oG4kF1xAHVbXrQNWrZq7X1aGYRiGYSyg1ZCIYBibQD7mQ1g27wSI1auBX/4S+O//Nt8FQ9XHOHCAblmAMIcKxKspyjiXUAJENQ6Iq66ihZkeFY7299N3VaXAe72zN6ZqYAGiHO97H/D1r1POwHxM3qpjXC76B1oJu51qTCxfXvk1n/oU/QO95x7Kpvna1yjdA6AfyptuorTUq6/mlj8MwzAMM+/RBIiG1AA+ja9it20tXsydnw+65wt/+7fAiy8C73oXnS9ZcUDs30+3LECYY6EJEKq+XTUCBGOc1la67esj8efVV4Ezzpi7AoRttgdQl5x9NrBpE6VhMHOSM84A/uqv6AsajdLtD39I1YgffJBcGO3t9Pdf/iVw333Arl16ThXDMAzDMPMETYBw33s31uE1/G3uc7DZbUVOy/mAzwf84hdUSyuRMN8FA2ABwipKgCjsgDGfqUUKBmOcQgcEQKnpGzfO3niqhR0Qlbj1VuDDH577nzADl4tSMNavp6sC3/kO1Zq4916qSfHAA9QxBKDiQStWUD2M1aupWGZ3Ny1dXXNXaWQYhmGYBYtq4/nNb+KwbTnuzf3veVuwOhymNIzrr6fzHqMoAYJTMKzR2UnnjpdfPtsjOT10dgIf+Qg1D2RmnkIBYmQEOHSIamjMVViAqMTNNwOf+ARVPGQBYl7hctEPpvrRjMeBN96gIpi7dwOvv073f/pTXZhQ+P1Uy6Klha40CEHtiJxOclXceivb0RiGYRimrlACRCqF77X+ObJ9jnlX/6GQjg66fmYGFiCqw+slJ+1CQQjgm9+c7VEsHAoFiO3b6f5cDk9ZgKhEYyMVC/jhD4GPf5w8/cy8xOsFNmygpZB0Gjh+nIpeHjlC9wcG9OKXiQQVv8xkKCfr4x8HPv954I//mFTJ9nbattdLIgXDMAzDMLOAEiA6OvB453uBPsy7+g/Voqz0LEAwTP3h8dAFzv5+XVxkAWK+8vnPA489Blx8MfDII3P7k2ZM43QCS5fSYoQXX6TmKXfeSbeFBAJkV+vooNtQSBcnfD56bMkSWrq66IeGYRiGYZgaEAgAmzcDH/oQ/P9JhRHmswPCCnY7nZuMjlLtCE45ZZj6oqWFBIjBQbrI2d4+2yOyDgsQU7FuHfD008A11wCXXUb9Ha+4YrZHxdQp559PjVO++lXKv4xGKb0jHgeGh4GTJ4ETJ6juxNgYOSjicWotOpnOTr3bR3MzOS1Uq1GbjWpVOBx0ktDVRQadM84ggaPe3BbpNKW0vPQSLT09wNvfDvzRH019gpPNAjt2kECzeDGlvzAMwzCMaYSgqwQAQg/TQyxAlBKJkADR1DQ/62MwzFympYUc1ydPzv1r4ixATMfq1cCzzwLXXkvLF78InHsulX9dulT36R87BgwNAeedt3B68DBl6eykNAyjJJN0CB09SsuRI1Rc5uBB4PHHSbwQQl9yOUr7SKdLxYvC1wlBwXtjI51MNDXRfbU0NNA2lBASjwMTE7REoyRkhMPlF4+HDvnDh2kZGKDX22x0FSUapccGBkipVeNsbKQTnPe/H/jMZ+j2He+gqy4+HwkqL70E3H8/1eBQ1X4BWm/ZMmDrVuobvXUr2dF6eynvcvdu2ueODlKFOzroq2i3V/uJLmAOHACefJIm8oor6k/dYhiGMYlKvWABopRIhP6nc/oFw9QfLS3UpWbfPuAtb5nt0VQHCxBGWLQI+N3vgD/8Q0rLUNhspVUKAboUfeWVwJYtFAW1ttLicFBkFo0CsRhFT21tFJXxif3skslQFOtw0De8qem0Ra5ut+52MEs6TYLFgQO0nDypuyVyORIVBgf15eBBEjSGh4sPXZUOEgjQ4vfT82+8QdV2R0bKOzVcLtLhWlroPbNZWvx+agOmCnauXUvuV7WPTz5JxYu+9jXgH/+xdLuBgF4oVEoSO3p6gD17qIvJv/wLfTzBII2tEl4vddU95xx675EREjUGBmj/lFCh6nUAJGLY7XSS2txMSzBIh0gqRXOu0mYcM/0LmkrRh3r8ONlngkHaGeW7i0ap59q99wLPPEODXbyYfrPOOINsOZs362fco6PUk3bPHvqALriguFH80BClnf3mN8Cjj5ISpli5Erj9duB979PzqWvJxAT9Dvp8td3m008DO3fSvF10EWrady+XIzvT+Lhua1q/nvK3GIapS0IhumUBohQ1JyxAMEz90dJCp3wAOyAWDpEIXY7u7yfpad8+ivi8Xv2EPxgEnnuOXnfvvcC//ZuxbdvtJFB0demFACIROqEdG6OgweOh9+nqotv2dlqnpYUi2FyOTrbHxiho8XhocbspcopG6flYjB7z+2lxuWid4WGKzpJJfT23uzTCSqeLl1RKXxIJ/XJ6IkHPZzL65fpYTL/Enkjol8ttNr26SiBAt14vvb/HQ8+fOKHbAyYmaB5UgYZwmPZfRcADAxS0nTxJl8dVBD06StHl2rUUJKxbR5/nE08ATz1FQYRCCPoP3NpKIlFrK42rcL+XLQPe9CbgkktKcwmkPC3+RaeT2oauWGFuvVyOPg6nkw6B6YYqJR1Co6M0lbEYHfLt7da0s8svp+XoUXI8xGK0xOO0L1dfXbkORiJBX7PHHqOPes0a+kjXrKHDqbeXlmPHKPVj+3ZyVAwN0b62tOjVhPe9NIJI/14sl/thRxYTCGACASThRjt60YUeLMFR2JDD3XgPtmFzfhw2G4kXXV1aN5TRYZw38Cusjz2PXGs77KtWILxpBVpWhuE42QPn8cNwnTgMceI40HsS7qFe+GL9GG89A5E3X4L2t11CosBLLyH36GNIPPw4fAcrlNRubQXOPBP4/e9p0jo6gOuuo2NY7fiJE/rrV6+m73ahoADQb8DWrfT8008D27bRh93QQI6HO+6g25dfBr79bfr7L/6C6uJceimtu3w55RU99RQtsRgF+xs20PcsnSbl69AhElICAfpuNTfTGHbsIFFk/376gK64QlefJhdgyWTodTt30m+w+rB7e+mgVvaeQIA++BdeoHUUdjs52DZsoO+sy0XLxIS+nb4+mpeWFv03NhLRl1iMKkC98gq9RyxWPMZFi6g32W23zYxQwzBMVSg9lotQlqLmhAUIhqk/Wlv1+3NdgBBSJZbXEZs2bZLbtm2b7WFURyZDJ9yqZUJfH52Iq8vLPh8F/n19wKlTdOKrPPhHj1JQ4fFQINDQQH+fPFneceH10vP1jt+vX2JXoom6ZJ5MUvA0MVF+HwE6+e/upvWPHqVAq9JrHQ69QkskQv9VQyF6n9deo16bySS9dtUqCnouvZQCFPV59fXp90+dorG5XBS1OxwkQKXTtC9bttBYTp2iZWSEtqUi/PZ2eg+12O26kHXoEAV96rUuF332Xq9+6/Ppi3LUlIv8MxnatlHxQ1kllGAyU66PiQm9ncjJk/Q+al/DYQqAFy2qPO5EgoLXU6d0gUu1ISmcq0SC5n54mN5z8WJg9WrIM1YglnLA98YrEE88TiLhK68U53hUIO0JANksnOk4BpaehwNv+hB6Oi9AdN8JZI8cg+PEUWwYfhxrR5+FXWaRsnvgyiYqbq8fzTglOjDma0fC34Sm/t1YJ3fABv23OAYvnsKleA5bcMrZhUWbO3HumztxVvswvPtehXfPdrgO7sFQ90a8etb/xovOSzA8Zsell5ImFgqB5uGll4AXXoB88SXA5YI4dyMF36tWUeD/6KOk5Bw4QG6IN72JlvPPL2/v2LED+MEPgN/+loLvwv8ffj99D0IhXVAopLGRPg+VnzM6So+vWEGCxTnn0Jh/8Qtg7156zuWi7YXDdH//fv17C9Bz7e0kEtrtpDINDdF2zjqLnGhXXUXb3r6dnGy/+x05QJRwmkzS90pZYVpaSFRQ3//+/tLf10CA/vtv3EgiifqdzmSA73+f5tXnI7fIF75Q00pRQojfSyk31WyDTEXmxbkIU8I3vwl87GPAXXcBH/jAbI+mvnj/+4HvfY/003/919keDcMwhXz968CnPkWnG8PDs2+er+Z8hAWIekRKCmwLrdEAndyePEmBtwqK+/roKAwE9JNgl4tOqlWQ5nAUCx/JpJ4KkkzSSbwK0t3u4nULA3wVJBcubrceSLpcupff46G/HQ4KDIwGxVLqBQmSSVoyGTqBDwaLX5tO01xMTGiXoG26c6G5eepvZiZDV2VVeworRKMUzPzmN3Tl1+ulQEil1WSzulti/356baHLohocDt2CEI3qgVdCC3zV5+Pz6ceFErIKg7TJ33+7XXe/qEW5aXy+YhFJuVWUoFToDhkcJMFA1UcZHJx+nwIBEiKCQf2zTyRInDOy/lQIQZ+Pulq9Zg0Fy6tWkZNgxQraB+XQicdpbru66PsxPk4teb/1rfKNvjduBG64Abj+egreYzHk9h9E/3P7MXpkGLlFSyC7l0J0L0HTIg+am/Wvw9AQcO9do3jlW8/B37MbhxvPQ9P1F+Cq691oaqLatw88QF99I7vncJApZ/Vq0rYOHKCcXikptlaH6KJFeteVlsYsRifs+UNjcFCv4TEwQNtXH3UoRBrG1vUjOC/5LFwnDlOax8aNxaLF+DiJfV4vuYWU71mhjpdyKRd791Il1+PHSagYHdXtMevW0bJ6dW3TNaYimdRzl5xOcn1M9fuyYwflCd1/P4mMynJTA1iAOH0s+HOReco99wDveQ/w3/8NvO1tsz2a+uLTn6bUyM99Dvjyl2d7NAzDFHL33cB730vm0yefnO3RsADBMHODTIYs808+SYHaypUU/C5bRn+rgKxQAEok9PyEWIzEhlOnKKg/dozuBwJ6pclAQC9WkEpR0KZSecbGKBhUNvVQiN5XCTdS6oF/4VI4DiVcTUzojpXJV4cdDhK0VGrSokXkXFEpM52dJGwpsaK/n65I79lDlSRVmpASPlS0rFKPfD79OaA49cft1q3yPh85Zd54g7Y9PEw1AC6/nLZpBSmpKG1Pjz6mzk563yqRkkSGjo5SrS6XowLuPT10Xy3t7TSlXV30MT73HPDww8BDD9Frly/XO6TYbLpmqVJUentLNSiAtEiVqqKsuOrjHhggQQMgjXHVKm0Vk6sAACAASURBVNqGOuSUjrR6NT3n8+mmlXhc1xNGR2l7Kksrk6GpfNObyLhQaI9WKUBGU4bqhmi05u1bWIA4ffC5yPzkwQepgNujj5JBitH58peB//N/SIS4447ZHg3DMIU88ghd5/rYx4BvfGO2R1Pd+QjXgGCY04XDQTb3Cy4o/7xyHNSySN7pQAkeKjXFSnQ4U+1tm5pqmygnBNU/mAGEqGzGsdmACy+kZSouvZSWr3zF2HumUmQyGBiggL+xkW6ny8Tp7ycd5plnSDMqzB4aHqYMjPvvr5wh5fHQ+wQCxSapxx+nIqN2O5kqXC69/qbSuZTbw+crNuGoepCqJqTfr2ttkYhuyHI66b1VmYq1ayvXGwFID1NZcr29ZGxSdVem/apy71iGqTu2bgX+9E+n/z1diHARSoapX9Q54nnnze44agELEAzDVIfDcRraQTAzgctFBpxly8yt19IC3HgjLZVIJinLKZ3WDSteL2UCVTKMpNPA888Dv/41iRFSAps2kdmktZWCf5WhVVjTdnychImuLtp+IEDPq5SSo0dJbFH1cPv7aT2AxI62Nj2ryOej51QaylTldTo6aC4K96+zU5/TZcvoRGEqgYNhmNNLKATceedsj6I+4SKUDFO/rF9PqWNzvQUnwAIEwzAMMwO43VQH0gxOp+7i+NKXZmZcALklDh2iupTbt1PqixIzolHKrDnnHL2cjKpn29ZGQofqw71vHzk+VKbS0BC5P3p79fc6dKi0mQfDMEw90t1Nt/ybxTD1hxDzp24NCxAMwzDMgsJm02tjvPWt5tefLqsnHqeGL4cOkZjBMAwzF7joIqoBvHLlbI+EYZj5DAsQDMMwDFNDvF69ECfDMMxcgsUHhmFmmlnuIMowDMMwDMMwDMMwzELAkAAhhLhWCPGGEGK/EOKzZZ4XQoj/qz2/QwhxrtF1GYZhGIZhGIZhGIaZ/0wrQAgh7ADuBHAdgDUA3imEWDPpZdcBWKkttwH4tol1GYZhGIZhGIZhGIaZ5xhxQJwPYL+U8qCUMgXgvwBMbr52I4C7JfE8gLAQosPgugzDMAzDMAzDMAzDzHOMCBCLAPQU/H1Me8zIa4ysCwAQQtwmhNgmhNjW399vYFgMwzAMwzAMwzAMw8wVjAgQosxj0uBrjKxLD0p5l5Ryk5RyU0tLi4FhMQzDMAzDMAzDMAwzVzDShvMYgK6CvxcDOGHwNS4D6zIMwzAMwzAMwzAMM88x4oB4CcBKIcQyIYQLwM0Afj7pNT8H8B6tG8aFAEallCcNrsswDMMwDMMwDMMwzDxnWgeElDIjhPgIgF8BsAP4vpRylxDidu357wB4GMD1APYDiAG4dap1Z2RPGIZhGIZhGIZhGIapW4ykYEBK+TBIZCh87DsF9yWADxtdl2EYhmEYhmEYhmGYhYWRFAyGYRiGYRiGYRiGYZiqEGReqC+EEP0AjtRwk80ABmq4vfkMz5VxeK6Mw3NlDJ4n4yzUueqWUnKrqNPADJyLAAv3uJ1JeE5rA89jbeH5rA08j7WnVnNq+XykLgWIWiOE2Cal3DTb45gL8FwZh+fKODxXxuB5Mg7PFTMX4eO29vCc1gaex9rC81kbeB5rTz3MKadgMAzDMAzDMAzDMAwz47AAwTAMwzAMwzAMwzDMjLNQBIi7ZnsAcwieK+PwXBmH58oYPE/G4bli5iJ83NYentPawPNYW3g+awPPY+2Z9TldEDUgGIZhGIZhGIZhGIaZXRaKA4JhGIZhGIZhGIZhmFmEBQiGYRiGYRiGYRiGYWaceS9ACCGuFUK8IYTYL4T47GyPp14QQnQJIZ4QQuwWQuwSQnxce7xRCPEbIcQ+7TYy22OtF4QQdiHEK0KIX2h/81yVQQgRFkL8RAixRzu+tvBclUcI8Qnt+/eaEOI/hRAenitCCPF9IUSfEOK1gscqzo0Q4nPa7/wbQohrZmfUzHzD7P9KIUST9voJIcT/q7DNnxce12WeP08IsVM7nv+vEEJoj9+uPb5dCPG0EGLNTOzzTFNPc1rw/NuEEFIIMWfa/dXTPAoh3ieE6NeOze1CiPfPxD7PJPU0n9pz7xBCvK6N5ce13t+Zop7mUQjxzwXH5F4hxMhM7PNMU2dzukTb9itCiB1CiOut7NO8FiCEEHYAdwK4DsAaAO8Uc/Qf9gyQAXCHlPIsABcC+LA2N58F8JiUciWAx7S/GeLjAHYX/M1zVZ5vAPillHI1gHNAc8ZzNQkhxCIAHwOwSUp5NgA7gJvBc6X4AYBrJz1Wdm60366bAazV1vmW9vvPMNVi9n9lAsAXAHyq3MaEEH8IYGKa9/w2gNsArNQW9T34sZRynZRyA4B/BPBPlvdqdqmnOYUQIgj6LX7B6g7NEnU1jwDulVJu0JbvWtyn2aRu5lMIsRLA5wBcLKVcC+DPqtiv003dzKOU8hPqmATwTQD3V7Njs0jdzCmAzwO4T0q5EXTe9S0rOzSvBQgA5wPYL6U8KKVMAfgvADfO8pjqAinlSSnly9r9cVCQuAg0P/+hvew/ANw0OyOsL4QQiwHcAKDwnyrP1SSEEA0AtgL4HgBIKVNSyhHwXFXCAcArhHAA8AE4AZ4rAICU8ncAhiY9XGlubgTwX1LKpJTyEID9oN9/hqkKs/8rpZRRKeXToBPAIoQQAQCfBPC3ld5PCNEBoEFK+ZykKuF3F2x7rOClfgBzsop4Pc2pxpdAgk7J9uuZOpzHOU2dzecHANwppRzW3quv+j08PdTZPBbyTgD/aXW/ZpM6m1MJoEG7HwKdt5pmvgsQiwD0FPx9THuMKUAIsRTARpD63yalPAnQAQ+gdfZGVlf8C4DPAMgVPMZzVcpyAP0A/l2zZ31XCOEHz1UJUsrjAL4G4CiAkwBGpZS/Bs/VVFSaG/6tZ2acGvyv/BKArwOITfGaRaDjV1F0LAshPiyEOAAKmD9mYvh1yWzPqRBiI4AuKeUvzI69npjtedR4q2bJ/okQosv46OuPOpjPMwGcKYR4RgjxvBBishtwTlAH86jG0Q1gGYDHDQ69bqmDOf0rALcIIY4BeBjAR42PXme+CxCizGNz8orBTKEpYf8D4M8mXV1hNIQQbwbQJ6X8/WyPZQ7gAHAugG9r9qwoFm4KwZRouXo3gv4pdgLwCyFumd1RzVn4t56ZUar9XymE2ABghZTygeleWuax/LEspbxTSnkGgD8HWWHnLLM9p0IIG4B/BnCH2feuJ2Z7HrXbBwEslVKuB/Ao9Kuyc446mU8HyPZ+OejK/XeFEGGzY5lN6mQeFTcD+ImUMmt2HPVEnczpOwH8QEq5GMD1AP4/e/cdH1WZ/XH889BRQBEUKVJEVMBEUFRQsURdG2BX7K5rQRBEcQXBggVBFEVURN2fuuraFhWQKJYIVlggiFQbHUVBQHpJ4Pn9cWYgCQmkzMyd8n2/XnlNmJnMnAw3M/eee855Xgu9l5ZIsicglgJ5s7ANKGWpSDJyzlXENuT/eO/DfVF/hEpvwiU4CVP2FUUnAJ2ccwuxNp4M59zr6LUqzFJgqfc+3Es7EktI6LXa1enAAu/9Cu99DtabeDx6rXanqNdG7/USNRH6rGwHHB36HPkaO7s5wdlw4/CQtAexbblBnp8ralt+iwQuf4+T17Q6cAQwIfQYbYExLrEGUcbD64j3fqX3fkvo+heBoyPw68VcvLyeodtGe+9zQm2FP2IJiYQQR69jWGcStP0iLI5e038A7wB47ycCVYDaJf19kj0BMQVo5pxr4pyrhG2AYwKOKS445xzWpz/Xe593kNUY4NrQ99cCo2MdW7zx3t/tvW/gvW+MbUOfe++vQq/VLrz3vwNLnHOHha46DZiDXqvCLAbaOuf2Cv09nob19em1KlpRr80YoLNzrrJzrgm2ozY5gPgkyUTqs9J7/5z3vl7oc+RE4Cfv/Sne+21+5+C++0JltOucc21Dz31N+LGdDaYLOxf4ORK/Y6zFy2vqvV/jva/tvW8ceoxJQCfv/dSI/sJREi+vYyiWunkeshP5B3YnhHh6PYFRwKmhuGpjLRnzI/ObRlecvY6E9kdrAhMj9TvGWpy9poux/VWcc82xBMSKEv9S3vuk/sLKQ34C5gH9go4nXr5CG54HZgDTQ1/nALWwSao/hy73CzrWePrCyuHGhr7Xa1X4a9QKmBratkZhb/x6rQp/rR4AfgBmAa8BlfVa7Xht3sRmY+Rg2fh/7O61AfqF3ud/BM4OOn59JcdXaT4rgYXYANX1oW23RYHHbAzM2s1ztgm9J8wDngFc6PqngNmhGMYDLYN+fRL9NS1wnwnYqkSBv0aJ9joCA0Pb5vehbfPwoF+fBH89HbbKzRxgJtA56NcnEV/H0G39gUFBvy7J8ppiq0p+E/pbnw78rTS/U/jBRERERERERESiJtlbMEREREREREQkDigBISIiIiIiIiJRpwSEiIiIiIiIiESdEhAiIiIiIiIiEnVKQIiIiIiIiIhI1CkBISIiIiIiIiJRpwSEiIiIiIiIiESdEhAiIiIiIiIiEnVKQIiIiIiIiIhI1CkBISIiIiIiIiJRpwSEiIiIiIiIiESdEhAiIiIiIiIiEnVKQIiIiIiIiIhI1CkBISIiIiIiIiJRpwSEiIiIiIiIiESdEhAiIiIiIiIiEnVKQIiIiIiIiIhI1CkBISIiIiIiIiJRpwSEiIiIiIiIiESdEhAiIiIiIiIiEnVKQIiIiIiIiIhI1CkBISIiIiIiIiJRpwSEiIiIiIiIiESdEhAiIiIiIiIiEnVKQIiIiIiIiIhI1CkBISIiIiIiIiJRpwSEiIiIiIiIiESdEhAislvOuVOcc0vz/Hu2c+6U4txXREREREQkTAkIESkR731L7/2EoOMQERGRxOacW+icO72Mj3Gdc+7rSMUkItGlBISIiIiIiIiIRJ0SECIpwjnXxzk3ssB1Tznnhjnn/u6cm+ucW+ecm++cu3k3j7PjbIVzrqpz7hXn3Grn3BzgmCj/GiIiIpIEnHOvAQ2BD5xz651zdznn2jrnvnXO/eWc+z5vy2eo0mF+aF9lgXPuSudcc2AE0C70GH8F9OuISDEpASGSOt4EznHO1QBwzpUHLgXeAJYDHYAawN+BJ51zRxXjMe8Hmoa+zgSujULcIiIikmS891cDi4GO3vtqwH+ATOBhYD/gTuBd59z+zrm9gWHA2d776sDxwHTv/VygCzDRe1/Ne79vEL+LiBRf3CYgnHMvOeeWO+dmFeO+DZ1z451z3znnZjjnzolFjCKJxHu/CJgGnB+6KgPY6L2f5L3P9N7P8+YL4BOgfTEe9lJggPd+lfd+CbZzICKSNLQ/IhIzVwEfeu8/9N5v995/CkwFwn9H24EjnHNVvffLvPezA4tUREotbhMQwCvAWcW87z3AO9771kBnYHi0ghJJcG8Al4e+vyL0b5xzZzvnJjnnVoXKF88Bahfj8eoBS/L8e1EkgxURiQOvoP0RkVhoBFwSar/4K7Q/ciJQ13u/AbgMq3ZY5pzLdM4dHmSwIlI6cZuA8N5/CazKe51zrqlzbpxzLts591WeNx6PlY4D7AP8FsNQRRLJf4FTnHMNgAuAN5xzlYF3gceBOqHyxQ8BV4zHWwYclOffDSMcr4hIoLQ/IhJVPs/3S4DXvPf75vna23s/CMB7/7H3/gygLvAD8GIhjyEicS5uExBFeAHo7r0/GusLC59Z6A9c5Zxbih04dQ8mPJH45r1fAUwAXgYWhHonKwGVgRVArnPubOBvxXzId4C7nXM1Q0kN/e2JSCrQ/ohIZPwBHBz6/nWgo3PuTOdceedcFefcKc65Bs65Os65TqFZEFuA9cC2PI/RwDlXKfbhi0hJJUwCwjlXDRs481/n3HTgeSwDClZS/or3vgFWOv6acy5hfjeRGHsDOD10ifd+HdADSyasxlozxhTzsR7A2i4WYHMjXot0sCIi8UT7IyIRNRC4J9RucRlwHtAXOymyBPgndrxSDuiFVRWtAk4GuoYe43NgNvC7c+7PmEYvIiXmvI/fqiXnXGNgrPf+iNDk/h+993ULud9s4KzQEDycc/OBtt775bGMV0RERJKP9kdEREQiI2Gy8t77tcAC59wlAM4cGbp5MXBa6PrmQBUscyoiIiISMdofERERKb24rYBwzr0JnIJN4v8DuB8rsXoOK3WsCLzlvX/QOdcCG0RTDRtEc5f3/pMg4hYREZHkof0RERGRyInbBISIiIiIiIiIJI+EacEQERERERERkcRVIegAClO7dm3fuHHjoMMQERGJK9nZ2X967/cPOo5UoH0RERGRwpVlfyQuExCNGzdm6tSpQYchIiISV5xzi4KOIVVoX0RERKRwZdkfUQuGiIiIiIiIiESdEhAiIiIiIiIiEnVKQIiIiIiIiIhI1JV5BoRzrjwwFfjVe9+hwG0OeAo4B9gIXOe9n1bW5xQRkcSQk5PD0qVL2bx5c9ChJJQqVarQoEEDKlasGHQokoe259LR9iwiImGRGEJ5GzAXqFHIbWcDzUJfxwHPhS5FRCQFLF26lOrVq9O4cWMsJy174r1n5cqVLF26lCZNmgQdjuSh7bnktD2LiEheZWrBcM41AM4F/lXEXc4DXvVmErCvc65uWZ5TREQSx+bNm6lVq5YO1krAOUetWrV0lj0OaXsuOW3PIiKSV1lnQAwF7gK2F3F7fWBJnn8vDV23C+fcTc65qc65qStWrChjWCIiEi90sFZyes3il/5vSk6vmYiIhJU6AeGc6wAs995n7+5uhVznC7uj9/4F730b732b/fffv7RhiYiIiIiIiEgcKksFxAlAJ+fcQuAtIMM593qB+ywFDsrz7wbAb2V4ThERkaiqVq1a0CGIRIy2ZxERiSelTkB47+/23jfw3jcGOgOfe++vKnC3McA1zrQF1njvl5U+XBERERERERFJRGWdAbEL51wX51yX0D8/BOYDvwAvAl0j/XwiIiK707t3b4YPH77j3/379+eBBx7gtNNO46ijjiItLY3Ro0fv8nMTJkygQ4edq0vfeuutvPLKKwBkZ2dz8sknc/TRR3PmmWeybJly6xIb2p5FRKTYNm6EOBsCHIllOPHeTwAmhL4fked6D3SLxHNI8MaNg9tvh6lTYe+9g45GRBJNz54wfXpkH7NVKxg6dPf36dy5Mz179qRrV8uBv/POO4wbN47bb7+dGjVq8Oeff9K2bVs6depUrGF5OTk5dO/endGjR7P//vvz9ttv069fP1566aVI/EqSILQ9i4hI3HviCRg+HGbPhpo1g44GiFACQlLDSy/BDz/A99/D8ccHHY2ISPG0bt2a5cuX89tvv7FixQpq1qxJ3bp1uf322/nyyy8pV64cv/76K3/88QcHHnjgHh/vxx9/ZNasWZxxxhkAbNu2jbp1tcK0xIa2ZxERKZb16y2r3bZt3CQfQAkIKaacHPj4Y/t+xgwlIESk5PZ0ZjeaLr74YkaOHMnvv/9O586d+c9//sOKFSvIzs6mYsWKNG7cmM0FShQrVKjA9u07V5kO3+69p2XLlkycODGmv4PEF23PIiIS1154AVauhH79go4kn4jPgJDk9M03sHatfT9zZrCxiIiUVOfOnXnrrbcYOXIkF198MWvWrOGAAw6gYsWKjB8/nkWLFu3yM40aNWLOnDls2bKFNWvWkJWVBcBhhx3GihUrdhyw5eTkMHv27Jj+PpLatD2LiMhubd4Mjz8Op54K7doFHU0+qoCQYsnMhEqVoHlzq4AQEUkkLVu2ZN26ddSvX5+6dety5ZVX0rFjR9q0aUOrVq04/PDDd/mZgw46iEsvvZT09HSaNWtG69atAahUqRIjR46kR48erFmzhtzcXHr27EnLli1j/WtJitL2LCIiu/XKK7BsGbz2WtCR7MLZnMj40qZNGz916tSgw5A8mjeHgw6CQw6BN96A1auhGLOtRCTFzZ07l+bNmwcdRkIq7LVzzmV779sEFFJKKWxfRNtz6em1ExGJkZwcOPRQqFMHJk6MykFbWfZH1IIhezR/vg2fPPdcSEuDNWtgyZKgoxIREREREZF83nwTFi602Q9xeMZYCQjZo8xMuzz3XEhPt+81B0JERERERCSObN8OAwfaQVuHDkFHUyglIGSPMjPhsMOs/eKII+w6zYEQERERERGJI++9Z6XrffvGZfUDKAEhe7B+PYwfb9UPAPvsA40aqQJCREREREQkbngPjzxi8x8uvjjoaIqkVTBkt7KyYOvWnQkIsDkQqoAQERERERGJEx99BN99By+9BOXLBx1NkVQBIbuVmQnVq8OJJ+68Lj0dfvwRtmwJLi4RERERERHBqh8GDICGDeGqq4KOZreUgJAieQ8ffghnngmVKu28Pi0NcnOtvUhEREREREQC9MUX8O23cNddULFi0NHslhIQUqTvv4dff83ffgFaCUNEEs+wYcNo3rw5NWvWZNCgQQCMGjWKOXPmRPR5rrvuOkaOHBnRxxQpSNuziIjkM2AA1KkD118fdCR7pBkQUqSxY+3y7LPzX3/ooVYRoTkQIpIohg8fzkcffUSTJk12XDdq1Cg6dOhAixYtAoxMpOS0PYuIyA6TJ8Nnn8HgwVC1atDR7JESEFKkzEw45hhLpuVVoQK0aKEKCBEpoZ49Yfr0yD5mq1YwdOhu79KlSxfmz59Pp06duP7665k3bx5XXHEFY8aM4YsvvuDhhx/m3XffpWnTpvl+bu7cuVx77bVMnjwZgIULF9KpUydmzJjBgw8+yAcffMCmTZs4/vjjef7553EFlrtq3LgxU6dOpXbt2kydOpU777yTCRMmsGHDBrp3787MmTPJzc2lf//+nHfeeZF9XST6tD1rexYRiQcDBkDNmtClS9CRFItaMKRQK1bA//63a/tFWHq6KiBEJDGMGDGCevXqMX78eGrWrAnA8ccfT6dOnXjssceYPn36LgdrAM2bN2fr1q3Mnz8fgLfffptLL70UgFtvvZUpU6Ywa9YsNm3axNhwyVgxDBgwgIyMDKZMmcL48eP55z//yYYNGyLwm0oq0PYsIiI7zJwJY8bAbbfZygEJQBUQUqhx42wIZYcOhd+elgavvgorV0KtWrGNTUQS1B7O7MajSy+9lHfeeYc+ffrw9ttv8/bbbwMwfvx4Bg8ezMaNG1m1ahUtW7akY8eOxXrMTz75hDFjxvD4448DsHnzZhYvXkzz5s2j9ntIFGh7BrQ9i4gE6pFHoFo16N496EiKTQkIKdTYsXDggdC6deG35x1EecopMQtLRCSmLrvsMi655BIuvPBCnHM0a9aMzZs307VrV6ZOncpBBx1E//792bx58y4/W6FCBbZv3w6Q73bvPe+++y6HHXZYzH4PEdD2LCKSVH7+Gd55B+68E/bbL+hoik0tGLKLnBz4+GM45xwoV8QWkpZml2rDEJFEVb16ddatW7fb+zRt2pTy5cvz0EMPcdlllwE7D75q167N+vXri1wloHHjxmRnZwPw7rvv7rj+zDPP5Omnn8Z7D8B3331X5t9FRNuziEiKGTTIVga4446gIykRJSBkF99+C2vWFD3/Aaw6onZtDaIUkcTVuXNnHnvsMVq3bs28efOKvN9ll13G66+/vqNfft999+XGG28kLS2N888/n2OOOabQn7v//vu57bbbaN++PeXLl99x/b333ktOTg7p6ekcccQR3HvvvZH9xSQlaXsWEUkhixdbP/wNN+y6YkCcc+GMdTxp06aNnzp1akQea9s2WL7cDpgLDHSWItx1l7W2rly5+1kmGRmwYYMNqxQRKczcuXPVC15Khb12zrls732bgEJKKYXti2h7Lj29diIiEdS9O4wYAfPmQcOGMX/6suyPJH0FxNChUK8erF0bdCSJY+xYOPnkPQ9STU+HWbMg1BIqIiIiIiIi0fTHH/Cvf8E11wSSfCirpE9A1Ktnl7/9FmwciWLBApg7d/ftF2FpabBxI4RW9BIRSVjdunWjVatW+b5efvnloMMSKRVtzyIiSeyJJ2DrVujTJ+hISiXpV8HIm4BQ5d+eZWbaZXESEHlXwjjkkOjFJCKJzXuPi/MeuGeffTboEPKJx/ZIMdqeS07bs4hIhKxaBcOHw6WXQrNmQUdTKqqAkHwyM21bLs723LKlzdXQShgiUpQqVaqwcuVKHYCUgPeelStXUqVKlaBDkQK0PZectmcRkQh6+mlYvx769g06klJL+gqIunXtUgmIPduwAcaPh65di3f/vfayygethCEiRWnQoAFLly5lxYoVQYeSUKpUqUKDBg2CDkMK0PZcOtqeRUQiYN06eOop6NTJeuETVNInIKpVgxo1lIAojqws2LKleO0XYWlpqoAQkaJVrFiRJk2aBB2GSERoexYRkcCMGAGrV0O/fkFHUiZJ34IB1oahBMSeZWbayhft2xf/Z9LT4ZdfbBiliIiIiIiIRNimTTBkCJx+Ohx7bNDRlIkSEAKA9/Dhh3DGGVCpUvF/Li3Nfnb27OjFJiIiIiJxaONGGDMGbrwRWrSwqfzr1gUdlUjyeeklW34zwasfQAkICZkxA5YuhQ4dSvZzeVfCEBEREZEkt3SplYJ36AC1asF558Hbb0Pt2vDoo3DoofDKK7B9e9CRiiSHnBwYPBiOPx5OPjnoaMospRIQGlpdtLFj7fLss0v2cwcfbMMoNQdCREREJAlt3w5TpsD998NRR8FBB8Ett8CcOXDTTfDpp/Dnn/Dll/C//0GjRvD3v0PbtjBpUtDRiyS+11+HxYut+iHOl4EujqQfQgmWgNi61ZZNrVUr6GjiU2YmtGkDBx5Ysp8rVw6OOEIJCBEREZGksWEDfPaZnaEaOxZ+/912+tq1g0GDoGNHaN5814OhY4+Fb7+FN96A3r3t/lddZZUR9eoF87uIJLJt22DgQGjduuRniuNUylRAgNowivLnn5agLsnqF3mlp1sCQhUmIiIiIgkq3Fpx7rnWTnH++dZavTB74AAAIABJREFU0b49vPqq9Z9//bUlFlq0KPpMbLlylnT48Ufo2xf++19ry3jkEdi8Oba/k0iiGzkSfv7Z/paSoPoBlIAQYNw4Sx6UNgGRlgYrV1pyXEREREQSQLi14r777OxquLXihx/g5pt3tla88w5cfbUlJUqiWjUYMMBaNc4808rHW7SA99/XWSuR4vDeEneHHw4XXhh0NBGTMi0YoAREUTIzoU4dOPro0v183kGUdetGLi4RERGRVOI9fPyxneisVcu+ate2Y/mInPwMt1Z88IHtAIZbK44/fvetFWVx8MHw7ruQlQU9e9qBVEYGDB1qZ7FEpHBjx1qZ+b//bX+nSSIlEhDhg2IlIHaVm2sVEBdcUPrtOvzZMWMG/O1vkYtNREREJJV89VXhbd4VK+5MSBT3a7/9oEIFYMkSO5D54AP4/HPYsgVq1LCqhI4d7QlLWt1QGqedBt99B88/D/feC61aWcXFAw9oSJtIQd5bBVHjxnD55UFHE1EpkYCoUsXehJWA2NW338Jff5W+/QLsM6NePS3FKSIiIlIWkyfb5UcfWZ5g5crCv376aef3OTn5H8OxnTZMpSMfcF65saRvnw7AsqoH833jLsxr0ZHVR7SnZp1K1KoItb7Ln7jYe+8otppXqADdukHnztC/Pzz3nA2sfPBB6NIllDERET7/3FaVee45y0AmkZT5Kw8vxSn5ZWbaNn3GGWV7nLQ0rYQhIiIiUhbZ2dCwIZx1VvHu7z2sXw+rlmwg56NPqfLZWGpNyqTqX7+z3ZVjQZ3jebPOo3y+d0embz6clascK7Ng7ftFP2blynbiLtz+UZxqi5o1oXz5EvyitWrB00/brInbboPu3W0A5lNPWaWESKobMMDK+K+7LuhIIk4JiBSXmQknnWSVeGWRnm6fGbm5Sl6LiIiIlMa0aXDUUcW885IluLFjqf7BB1TP21px1lnQsSPlzj6bprVq0RQoWMCdk2PL0xdWXfHnn/n/PWeOXa5aZft5hXHOkhANG9rIh6ZN8182bFjESdwjjrCZFKNHwx13wOmn2+obQ4bYD4qkookTYfx4+zuoUiXoaCIuZQ4V69WzN1DZaeFCmD0b/vGPsj9WWhps3WolgS1alP3xRERERFLJ2rW2H3XVVUXcIbxqRXiew/ff2/VNm9oshY4d4cQToVKlPT5XxYo2gLxOneLH573FWFRbyJ9/2r7lnDl2gmvLlp0/W778rsmJnd879j3/fEucPPmknflt3hx69bKlB6tVK36QIslgwACrErr55qAjiYqUSkAsW2bv3Uk0RLRMMjPtsizzH8LyroShBISIiIhIyUy3UQ35VyVbvz7/qhV//GE7siecAIMHQ4cOtkRf1IY27OQc7LOPfe2pOGH7dqs8nj8f5s3Lf/n++7BiRf7777cfHHxwFQ4++G5a/f1aLsq+m0MHDiT3/16h3OBHKXf1ldqBl9Qwfbr9rT/0kA1kSUIplYDYts3e8EqS7U1mmZlwyCFw6KFlf6zDD7fs9owZcNllZX88ERERkVSSnW2XbeosgeEfWNJh/HgrJdhnH6sQ6NDBVq2I81UjypWDBg3s66STdr197VpYsGDXBMW0afDewnr0zf03x3ELTy2/jeOuu4bpXZ7l5SOfIvfo4wpUT6hAQpLMI49YK9WttwYdSdSkVAICLBurBIQtA/355zZwOBIqV7YkhFbCEBERESm5adPgvFpfc0C7DBvSkLe1on37pJqEX6MGHHmkfRWUmwtLl8L8+W2Z8fNEFn/wGqdn9eGp/7Xlje+updfWgfxO3R33P+CAgi0dOy/r1lXhhCSQH36AkSOhTx/Yd9+go4malExAtG4dbCzxIDyrqEOHyD1mWprNTBERERGRkpk2dTujcm+3M2WffBKz1op4U6ECNG5sXxkZ5eDma2HdhfDII1zxxBNcXu1dfr2uH/9r25Ofl1TZUT3x7bfw1lvW/hFWpQo0aVL47IkmTaBq1aB+S5FCDBpkG+3ttwcdSVSlZAJCrP2iWrXCy+JKKz3d3vjXrLFKQRERERHZsw0boPUPb9KMqfD0qzaEUXaqXh0GDoQbbsD16kWDZ+6mwYcvwhNPQO9OOxI1OTmwaJG1dBRs7/jiCxupkVe9epaMGDDAikxEArNwIbz+urVe7L9/0NFEVcokIA480C6VgLApxpmZcMYZxRqUXGxpaXY5a5bNRhIREYk251wV4EugMrZfM9J7f3+B+zjgKeAcYCNwnfd+WqxjFSnKzMmbGEBf/mp6FPteeWXQ4cSvpk1h1Cj49FPo2dOW7Dz9dBg6FFq2pGJFm292yCG7/qj3tlpHwaGYn34KV1xhq3dUrx77X0kEsKGy5crBnXcGHUnUlboryjlXxTk32Tn3vXNutnPugULuc4pzbo1zbnro676yhVt6FStaMmnZsqAiiB8zZ1pvXSRWv8gr70oYIiIiMbIFyPDeHwm0As5yzrUtcJ+zgWahr5uA52IbosjubX/yKRqxmJyBQzS0oDjOOMOWIR02DKZOtWESPXrAqlVF/ohzULs2HHccXH453HMPvPQSvPOO7Rf37x+78EXyWbbMNsbrrrPJrUmuLO9wxfnAB/jKe98q9PVgGZ6vzOrVUwUE7Fx+85xzIvu4Bx1krRczZkT2cUVERIriTbiwumLoyxe423nAq6H7TgL2dc7VRSQeLF9O63GPMK5SJ2pffErQ0SSOChWge3f4+We4+WZ49llb2u2552ySZTG1awc33QRPPbVzKVSRmBoyxPqHevcOOpKYKHUCopgf+HFFCQgzdqytMV03wrtezlkbhhIQIiISS8658s656cBy4FPv/f8K3KU+sCTPv5eGriv4ODc556Y656auWLEiegGL5PXAA1TM2ci7xw1OxZmTZVe7tiUfvvvOdkS7doWjjrIlTItp0CDYbz/LY2zbFsVYRQpauRJGjLCynKZNg44mJspU41WMD3yAdqE2jY+ccy1381hR/9BXAsK28UmTIt9+EZaebi0YPq5TUSIikky899u8962ABsCxzrkjCtylsMO6XT6pvPcveO/beO/b7J/kQ8AkTsydi3/+eZ53Xahz0mFBR5PY0tNtmbd334V16yAjAy66CBYs2OOP1qxp8ywnT4YXXohBrCJhTz1lU2jvvjvoSGKmTAmIYnzgTwMahdo0ngZG7eaxov6hX68e/PFHiaqyks64cbY8UbQSEGlpsHYtLF4cnccXEREpivf+L2ACcFaBm5YCB+X5dwMgxU9JSFy46y62Vdmb/v5+jj466GCSgHNw4YU2UfLhh23Ht3lzG/hQcAmMAq680nIWd98Nv/8eo3glta1dC08/DRdcAC2LPE+fdCIy5aaoD3zv/dpwm4b3/kOgonOudiSeszTq1bOD7+XLg4ogeJmZcMAB0KZNdB5fgyhFRCSWnHP7O+f2DX1fFTgd+KHA3cYA1zjTFljjvddYagnW55/D2LFMOaMff7I/Rx0VdEBJpGpV6NcPfvoJLr7Y1tk87DBb5rCIMl3nYPhw2LQJ7rgjxvFKaho+HP76y7bVFFKWVTD2+IHvnDswtPQVzrljQ8+3svThlk29enaZqm0Yubnw0Uc2fDJaA5aPCNXAaA6EiIjESF1gvHNuBjAFawkd65zr4pzrErrPh8B84BfgRaBrMKGKhGzfbsvtNWrEv/fpQa1a0LBh0EElofr1LenwzTd2IHD11bZW/JQphd79sMOsAuLNN215TpGo2bjR+n7OPJNUK38qy2FocT7wLwZmOee+B4YBnb0PbjpAqicgJk60JFu02i8AatSAxo1VASEiIrHhvZ/hvW/tvU/33h8RXnHLez/Cez8i9L333nfz3jf13qd576cGG7WkvNdes6GJAwcyeUYVjjoKDaCMpuOPh//9D15+GebPh2OPheuvL7TXok8faNbMZllu3hxArJIa/vUvWLEi5aofoGyrYBTnA/8Z731L7/2R3vu23vtvIxV4aaR6AiIz01YsOuOM6D6PVsIQERERKcLGjXbQceyxbLmgM7NmpdwJ0GCUKwfXXWdtGXfdZZURhx4KgwfD1q077lalilXG//ILDBwYXLiSxLZuhcceg5NOgvbtg44m5qJUiB+fDjjA3ntSOQHRvj3ss090nyc9HX78EbZsie7ziIiIiCScJ56AX3+FIUOYNduRk4PmP8RSjRrw6KMwezaccgr07g233prvLqefDldcYctz/vhjMGFKEnv1VVi6NCWrHyDFEhAVKkCdOqmZgFi0CGbNim77RVhamq2hPHdu9J9LREREJGH8/rsd1V54IZx4ItnZdrUSEAFo1gzGjIFeveDFF+Hrr/Pd/MQTsNdecMstWl5eIig3194D2rSJfll6nEqpBARYG0YqJiAyM+2yQ4foP5dWwhAREREpxP33W4noo48CMG2aVaYefHDAcaWyBx6wCaBdukBOzo6r69Sx48Tx461bQyQi3n4b5s2z6ocUHfyiBESKyMyEpk2t1S3amjWDypU1B0JERERkh1mzbPBct25wyCEAZGejAZRB23tveOYZa8l44ol8N914I7Rta0USq1YFFJ8kj+3b4ZFHoGVL6NQp6GgCowRECti40ZaaPvfc2HzAVagALVqoAkJERERkh3/+0+YP3HcfYCfbZ8zQAMq40LEjnH++VUMsWLDj6nLlYMQISz706RNgfJIcRo+GOXOgb1/buFJUyv3m9erZiid5ht0mvfHjbRmhWMx/CNNKGCIiIiIhn3wC48bBvffCfvsBdhyydavmP8SNYcOgfHkbSJln6MORR0LPnjYm4ttA1/OThOY9DBhgJemXXhp0NIFKyQQEFLrsb9IaO9aqy04+OXbPmZ4Oy5bBn3/G7jlFRERE4s62bXDnnTbooVu3HVeHB1CqAiJOHHQQPPggfPghvPdevpv697ebb74535gIkeL75BP7o+/Tx8rFU1jKJiBSpQ3De5v/cMYZNpchVtLS7FJtGCIiIpLSXnnFdogGDcq3MzZtGlSvvmMchMSD7t2hVSvo0QPWrdtxdbVq8PTTNsZj6NAA45PENWAANGgA11wTdCSBUwIiyc2aBUuWxLb9AnauhKE2DBEREUlZ69fDPfdAu3Zw8cX5bsrOhtatU7oVPP5UqADPP29lvPfem++m886zr/79bXl7kWL76iv7+uc/oVKloKMJXMq95aVaAiK8/OY558T2eevUgdq1VQEhIiIiKezxx63vd8iQfJPAc3Ph++81/yEuHXss3HKLlTxMm5bvpmHD7L+xwJgIkd0bMAD23x9uuCHoSOJCyiUgate25GaqJCDGjrXsejjxEivOWRWEKiBEREQkJf32Gzz2mA2ca9cu300//ACbNmn+Q9waMAAOOMCGPmzbtuPqhg1toYyxY2HUqADjk8QxdSp8/DHccQfstVfQ0cSFlEtAlCsHdeumRgJi5UqYOBE6dAjm+dPSbEnlPO/bIiIiIqnhnnus1GHQoF1uCp9YVwVEnNp3X3jySTt4fO65fDf16GEn2QqMiRAp3COP2PbUtWvQkcSNlEtAgFUDpEIC4uOPYfv22M9/CEtPh40bYf78YJ5fREREJBDff2/DJ3v0gCZNdrk5O9tOhh52WOxDk2K67DKb4t63b74Dh4oVbUzEr7/C/fcHGJ/Ev9mz4f33bbhpjRpBRxM3lIBIYpmZ1m50zDHBPH94EKXmQCSuWbNgxYqgoxAREUkg3tuymzVr2sFrIaZNs8UWypePcWxSfM7B8OGwdSvcfnu+m9q2hZtugqeegu++Cyg+iX8DB8Lee8NttwUdSVxRAiJJ5ebCuHFw9tnBTVdu0cLeuzUHIjHl5kL79rt85oqIiMjufPQRfPaZnR6vWXOXm7dts4NWzX9IAIccYq0077xjO9Z5DBxos+W6dFG7sRRi3jx4803bQGrVCjqauJKyCYjVq234T7KaMwdWrYK//S24GPbaC5o1UwVEopoyBf76y/ahNOlZRESkGHJzbam9Zs3swKMQP/8MGzZo/kPC+Oc/rVema1frLQ6pWROeeAImT4YXXggwPolPjz5q/Tq9egUdSdxJ2QQE2BK/yWrBArs89NBg40hLUwVEosrKsss//oC5c4ONRUREJCH83//ZWaBHH4VKlQq9S3a2XaoCIkFUrgwjRtjO9YAB+W664go47TS4+25bbVUEgKVLbQbM9dfb6geST0onIJK5DWPxYrts1CjYONLTrQJpw4Zg45CSy8qCAw+07z//PNhYRERE4t7atXDffda/eP75Rd5t2jSoUgWaN49hbFI2p5wC115ry6rOmbPj6vCYiE2bbJVFEQAef9xWArjrrqAjiUtKQCSpRYugalUbQhmktDQr3589O9g4pGQ2boRvv7XMfpMmSkCIiIjs0eDBsHw5DBliR6ZFyM62EzQVKsQwNim7xx6D6tWttSZPb+qhh9qs0TffhE8+CTA+iQ/Ll1tPzlVXQePGQUcTl5SASFKLFkHDhrv9/IsJrYSRmL75xoY+n3YaZGTAhAkasCRxwHtb1/fVV+Gnn4KORkRkpyVLLPFwxRW7XX5s+3YNoExY++9vSaavvrLy+jx697axH127JveMOSmGoUNh82bry5FCpWQComZNa+dK9gRE0O0XYGfP995bcyASTVaWnZk56SRLQKxebUuai8TcokW2o3fddXYmoWlTK4M9/3zIyQk4OBGRkH79LEn6yCO7vdu8edapoQGUCervf4cTTrDBlH/+uePqKlXguefs/3fgwADjk2D99Rc8+yxcfLENLpVCpWQCwrnkX4pz8WKrgAhauXJwxBGqgEg0WVlw3HFQrRqceqpdpzYMiYklS+C112xw08EHW9Lh73+HzEw49lh45hnby5s7F55+OuhoRURsqMNrr9m61Xs4+zNtml2qAiJBlSsHzz8Pa9bs0t9/2mlw5ZUwaBD88ENA8UmwnnnGMox9+wYdSVxLyQQEJHcCYvNmW7kgHiogYOdKGFrKMTGsXm39qaedZv+uW9cGZSkBIVHx22/wn//AjTfaeusNG8I118Do0dCqFQwbZm8gf/wB//0vdOsGN98M55wD/ftr7LiIBMt7W2avdm3o02ePd582zRbHaNkyBrFJdLRsCXfeCS+/DF9+me+mIUOs8veWW7Tfm3LWr7f2i3PPtf0XKZISEEkoXlbACEtPh5Urk3vZ02QyYYJ9aIYTEGBtGF9+qYp3iYDff4e33rIkwmGHQf36Nqhp5Egrl3rySZg+HVasgPfeg+7dLYtZLs/HlXP2Ib9lizXeiogE5YMP7IPzgQdgn332ePfsbHtLK2KFTkkU995rFXpdutjQrJA6dawCYsIEeP31wKKTILzwgh3w9OsXdCRxL2UTEHXrJu8B8aJFdhkvCYi0NLtUG0ZiyMqCvfaCtm13XpeRYUupTp4cXFySoNavh3fesclczZvbm+/ll1sS4tBD7XRRdrb10o4aBT17wpFH5k84FKZZMzvr+OqrtmSLiEis5eRYGf7hh1sV1x54bxUQmv+QBPbay3r95861JRfzuPFG24fq1QtWrQooPomtzZttOzj1VGjXLuho8pk1C959N76GyadsAqJePWvRWb8+6EgiL5yAiIcZELAzAaFBlIkhK8uWMM97dubkk+2ks9owpMS6dYPLLrNTQQcfbBPEp0yxvbIPPrCF0486CsqXL/lj9+sHDRrArbfG1yeriKSGF16AH3+097WKFfd494ULrc1R8x+SxDnn2LDBhx6y6ZMh4TERq1YVqytHksErr9iZ7TisfnjwQfjHP+xEYrxI6QQEJGcVxOLF9uZXv37QkZhatez1VgVE/Pv1VxuclLf9Auz/sFUrJSCkFL74Ajp0sD2xzEybHN6mTekSDgXtvbdVUHz3Hbz4YtkfT0SkuNassTk0p55q73HFEB5AqQqIJDJ0qCWfbr0139CH9HSbSfrii7a0uSSxnBx49FGb3p6REXQ0+fz8s3W4dusGNWoEHc1OKZ+ASMY5EIsWWfKhGMn4mElPVwVEIggnGAomIMDeU7/9VutbSwksW2ZvSKecYuu6RsMll9gBQL9+1nspIhILAwfae86QIVYiWAzZ2fZWGK4MlSRQvz48/DCMG2eDkvO4/3446CAbE6EZWknszTetvKlfv2K/F8TKY49B5crQo0fQkeSnBESSJiDiZf5DWFqatcnpDTi+ZWXBfvsVPrw3I8PmLKndXopt0iS7jGY/pHO2HOeaNXFZ+igiSWjhQjvzffXV0Lp1sX9s2jRbQKFKleiFJgHo1s3KWm67zT6LQqpVs1UZZ82y+cqShLZvt2RkenqxK6Fi5bff4N//tlXN69QJOpr8lIBI0gREvMx/CEtPt4PXn34KOhIpiveWgDj11MLn/7Vvb1XzasOQYps0yUqxol1v3LKlrZbxwgs7a5xFRKKlb1/7oBwwoNg/4r1VQGj+QxIqX96GPixfDvfck++mTp3gvPOsW2fhwkCik2h67z3rXe7bN+6qH5580sZj3Xln0JHsKmUTEDVq2ADbZEtAbNsGS5fGXwVEerpdag5E/Pr5Z9t2Cmu/AKheHY49VgkIKYGJE+3sYCxO9/XvD/vvb32427dH//lEJDVNnmwl17162RDcYlq61Bb70fyHJNWmjVVCPPusDVrOY9gwy1d1755vTIQkOu/hkUdsRa+LLw46mnxWr4YRI2wGeJMmQUezq5RNQDhnVRDJloBYtgxyc+MvAXH44db3qDkQ8Ssryy6LSkCAtWFMmWIryIjsVk4OTJ0au+Wo9tnHhkBNnAivvRab5xSR1OK9JR7q1LHlN0sgO9suVQGRxB56CA48EG6+2XbGQxo2hAcegLFjbbVpSRIffWRDsPv0icxg7QgaPtxWeuzdO+hICpeyCQhIzgREeAnOeEtAVKpkSQhVQMSvrCw7mdOsWdH3yciwKpuvvopdXJKgZsywiaWxXA/7mmts8fXevfP14YqIRMT778PXX9u6dtWrl+hHp02zs+DhilBJQvvsA089ZQelzz6b76bbboMjj7QqiHXrAopPIsd7a8Fq2BCuuiroaPLZuNE2w3POid/3GyUglICImbQ0VUDEq+3bYfx4q37YXQtbu3Y2TVdtGLJHEyfaZdu2sXvOcuVs4tfy5Xa6SUQkUrZuteRmy5Y21a2EsrOhRQtr/5UkdvHFcPbZNgti6dIdV1eoYCXxv/1mq2NIgvviC5vKftdd8bXsIPDyy7BihRVmxCslIH5Lrn6sxYvtMipDKFesgHnzSv3j6ekWn05Mxp/p02HVqt23XwBUrQrHH68EhBTDxIlQt27sJ+IefTTcdJM13c6eHdvnFpHk9dxz8Msv8PjjpVpWeNo0zX9ICc5ZIjw3F3r2zHdT27bWnREukkhF3sM77yTBfuSAAdaKVYpkZDTl5NjSmyecYMPj41WUFmZPDPXqWZnK2rVWNZUMFi2CWrVg771L+QDbttmY3rlzbapr3q+VK+0+Rx0FN9wAV1xRohcuvO71zJlw4omljE+iosj5D9u32x9Jbq69q+XkcP5RuTw1JIfVk3KpWS0n3207vt/ddZUrw0UXWTZDktekSVYyE8RU6AEDbA+nRw/47LO4m0wtIglm9WpruzjjDDjzzBL/+G+/we+/KwGRMg4+GO67z1ZGyMyEc8/dcdPAgbZwws03W54+zkYHRNUvv9jv/fnnsO++sGCBXSacyZNt32Lw4Ljbl33nHTsWfOaZoCPZvZRPQIB9MCRTAqJY7RcbNsCPP+5MLoQTDj/9ZGWGYQccYMMbLrrILgFeeQW6drVBTJdcAjfeaKm2Pezk510JQwmI+JKVZf+94b8JwNLUbdrskqbvEfqiLK39WVlWIybJaflymD8funQJ5vlr1bIkRNeuMHKkvU+JiJTWgAGWhHj88VIlNMOrA2sAZQrp1Qtef91WxjjllB1nBvfd15ZHvPJKW7mza9dgw4yFnBwYMsQ6IytVgn797E9qyBCb25lwBgyAmjWD28cpgvcwaBAccYTNf4hnSkBgCYjmzYONJVIWLYLDDgv9w3v444/8CYbwV7hXA6xvumlTOwI9+2y7DH/tt9+uT9KzpzUz/utf8MYb8Oqr9qQ33GBD4A44oNDYGjSwRI/mQMSXrVttqOTf/17ghhkzLPlwzTXQqpX1uFWoQG65inTtUZF27Svw95vsOipW3HF7vsvCrhs+3FYr6NgRLrwwkN9ZomzSJLuM5QDKgm66CV54Ae64wz6JS10WJiIpbf58ePppK7Uu5US3adMsb9GqVYRjk/hVqZINfTjpJDvKHjRox02XX27nYO6+23aDDjwwwDijbPJkO085Y4adyxw2zI6/fv7ZEjHduxd52BCfZs6EMWNs6e8SDqKNtg8/hFmzbCGwcvE+ZMF7H3dfRx99tI+Fn37yHrx/9dWYPF3Ubd/u/d57e3/bbd77337zvm5d+wXDX9Wqed+mjfdXXeX9ww97P3Kk97Nmeb95c+mfdP167196yfvjj7fnqFDB+4su8n7cOO9zc3e5e/v23p9wQumfTiLviy/sv+699wrc0L+/9855//vvu/zMOed4f/jhpXzCrVu9P/po72vVsu1Ukk+fPvZesHFjsHF8/bVt3H37BhtHBAFTfRx8TqfCV6z2RSTOXXKJ93vt5f2vv5b6ITp1KsNnpiS266+3z8OZM/Nd/dNP3leu7H3nzgHFFWVr19rxiHPe16/v/ahR+W+fO9f7cuW8v/32YOIrtc6d7Xhq5cqgI9nFCSd436iR7WbHQln2R+I9PxJVdevaZbKshLF6tXVWNGoEjB4Ny5ZZs9mnn8KSJTbsYsoUS43162epyJYtrSe/tPbe206df/ONDXzr0QMmTICzzrIeuAceyFdtEV4JwyfR4M9El5VlmdJTTilww+jRNnGyTp1dfiYjwwppSvW3U7GilSVu2AD/+Ic2hmQ0caKd6gu6N/KEE+Dqq61s+pdfirzb6tXwt7+l7lD5Qw8VAAAgAElEQVQwESnCt9/Cf/9rk+7z9SiWjAZQprBHH7Xy35tvtrlaIc2a2YiIt96CTz4JML4oyMy0w4thw6zFZM4cOO+8/Pc5/HC49loris2zWEh8+/lnG7LQtWvhFeIB+vprOxS78864W5SjUCmdgKhWDWrUSJ4ERL4lOMeOtQRA795w+unW/xDtQWwtWlhD16+/wttvW1tG//7QuLG1drz3Hq1abGXdup2xSvCysmzHqGbNPFcuWmRHY+efX+jPZGTY5fjxpXzSww+3Mb0ffWQlipI8cnMt0Rlk+0Vejz5qSdYC08jzeu45y9MOHx7DuEQkvnlvffx169pefSktX24HWJr/kKJq17Yk+Lffwksv5bupd2849FA7nt20KaD4Iuj33+Gyy6BDBzu++uYbG4ZYo0bh97/vPsvJJMQciM2b4dZbrbXmjjuCjmYXgwbZphZni3IUKaUTELBzKc5kED6ob1xnox1VdugQzPT3ypXh0kstpTt/vq2FPHMmXHQR1957EI9yF/M/+jH2ccku1q+H//2vkNUvRo+2y4Ip65Ajj7SERZmWUerWzaaJ9+plw08lOcycaSuntG0bdCSmbl1LhGZmWmK2gC1brL0b4P33bViWiAj//a/Ns3n44TLNkAkPoFQFRAq79lo4+WSrpFm+fMfVlStbAnzePHjkkQDjKyPv4f/+z+bpjRplCYVp0/Z8HqJxYysMeeml3RYpBm/9elvJ5NNPbYehkMrgIM2YYbs4t90Ge+0VdDTFowREEiYgmi783DJ1HToEGxBAkya2dNXChTB2LO6E47mDJ8joergN5nntNSvFl0B8+aWdsN4lATFqlNXPNWtW6M+VKwennlrGBIRz9qlTtSpcdZWO/JLFxIl2GS8VEGBTrpo3tyqIzZvz3fTmm3bW5pZbbKXhCROCCVFE4siWLdCnjw2dvPbaMj1UdrZdtm4dgbgkMTlnmYb16+Gf/8x3U0aG7QI9+qi1tiaan36y/cEbbrA/lxkz7LxjpUrF+/l+/axloH//qIZZen/9ZSfLJkyAf//bftE4M3iwVfV36xZ0JMWnBEQSJSAWL7ZjuRpfjrUt8aSTgg5ppwoV4NxzqfjB+7RrsJQ30gfZjIprrrHeuLQ069V+4gk7ql21KuiIU0JWln1InHBCnitXrbLMRBHVD2EZGZZXWrCgDAHUq2frUE2ZYssaSeKbNMnODjRuHHQkO1WsaM2o8+ZZm1iI9/aWk5ZmV1erZic9RSTFPfOMfbgNGQLly5fpoaZNg0MOSZ7l3qWUmje3CohXX92lf3XIECuy6dIlccZibd1qu23p6fD99/Dii/Zr7ViJr5gOPNDGx73xhq3gEFf+/NN2dqdMsdkPV18ddES7WLDA5ojcfHOBVuo4pwREKAGRKH/wu7NoETRq6HGZYy1bV5bhklFU76gDeTint6VNJ0ywswyNGlnioVcvOx1fq5YdwJx/vg2yHD3aMizJ8B8VR7KybM5kvpKtzEzYtq3I+Q9h4TkQZaqCALj4YntTf/hh6weRxDZxorVfBNH+tTunn27b2oABOwbjfvaZdYzccYclbzt2hPfeUzGOSEpbudI+j84+2943yig7W/MfJKRfP5vP1qWLVdmEHHCAVUB88YUVBse7SZNsm77nHjtXNXeuFQaUdunHu+6yFS3vvTeycZbJsmXWNjN3rh2DXHRR0BEVKpwjjcOxFLulBEQ9y+Ilwwn3RYsgY7/pNgQyHtovipCWZrmHzVuc/XE//LD1Zv/6K/zxB3z8sU1TadfO6tEeeMAOhhs1sgkrp51mA6Fef91W3sjNDfpXSkgrVljWutD2i/r197jHdPjhlrkucwICrKeufn2rQ1RLTuL6809r5Iyn9ou8wtUPoYFyQ4bYNnz55Xb1JZeoDUMk5T34oK0a9thjZX6olStt30zzHwSwTPfw4bYTPHhwvptuuME+Onv1su0mHq1bZx2Nxx9vnQljxtjM+QMPLNvj7ref/d6jRlmxQeAWLYL27e3yo48sGRmHli+32RvXXFOmRXoCUeoEhHOuinNusnPue+fcbOfcA4XcxznnhjnnfnHOzXDOxd1bcPg/LBnaMBYtgr/ljLUzj3H6xwJWrrVtmyUVd3HAAbYeXu/e1pz9ww+2I/Dtt/Dss5aBXLvWyiOvvhqOOMLSpscea/VHzz1nqVklJfYoXAGYLwGxaROMG2cp7T2ksp2zKojPP49AYco++1hZ4rx59ikkiWnSJLuMlwGUBTVsaOue/fe/LPhXFh9/bDtT4WKxs85SG4ZISvv5ZztAvPFGm4NURuEBlKqAkB3OPNOWihgwwLa3kHLlbFGw1autMDjejBlji909+6x9bs6ZY1WDkXL77XaOsV+/yD1mqfz8syUfVq60Msld1qiPH8OGWSFNgbEiCaEsFRBbgAzv/ZFAK+As51zBvc6zgWahr5uA58rwfFGRLAmITZvsjHab38fawXicTWjNKy3NLmfOLOYPVKtmaeGuXeGFFyw9un69PcCrr9r11artXJu3XTs7Ml6zJmq/QzLIyrLczTHH5Lnys89sBYM9tF+EZWTYAL+IDE46+WQ7M/3889YGIoln4kSrBWzTJuhIinbnnXDwwVS+szs1qubQpcvOm/K2YSiHKZKCeveGKlWs8jICwgkIDaCUfJ580jLfXbvmO4OTnm6l9P/6F3z9dYDx5bFsmVUHnneezRiYOBGeesr2HyOpenVLvHz6qbWiBGLWLEs+bNpkZ+ni9WQKO8/FXnSRLeWaaEqdgPBmfeifFUNfBc+Dnge8GrrvJGBf51zd0j5nNCRLAmLxYjiAP6i/dHJct1+ALaxQubJNyi21ChWs+uHqq62OOjy4cuFCS89OnGhHx3/+Gamwk05Wlh3zV6iQ58pRo2zB5pNPLtZjRGwORNhDD9kn8D/+YRk1SSyTJtkarWVYsi7qqlRh9f1DqbdmLi8d9Qz77Zf/5nAbRoEZYSKS7L780tbi7dMnYidxsrNtMbCC7zOS4urWtXU3P/vMJgjmcf/9VqzXpUuw84i2b7dzfs2bwwcfWMFGdjYcd1z0nrNrVzsu69cvgJFv2dm271u+vL0XtGoV4wBK5vnn7Txr795BR1I6ZZoB4Zwr75ybDiwHPvXeF5wgVx9YkuffS0PXFfZYNznnpjrnpq6I4YFH3VA6JNETEIsWwTl8aP+I8wREhQpW2VjsCojics7mRHTtagfSc+bYm0mi/+dGwaJF1u2Qr/1i2zb7lDn33GKvn9Skic0KzcqKUGCVK9tsj9Wr4aabNHQ0kWzbBpMnx/UZg7AhP3Ygk3O4YPr9VsKTh9owRFLQ9u1WHdWggdWCR8i0aZr/IEXo0sVKUG+/3QYqhOy9t53Znj3bVmkKwg8/WOfBzTfb9jtzpnUvVqwY3eetWtUGUX7zjY1eiJlvvrEzajVqwFdfWdYljm3ebNvG6afHd8Hp7pQpAeG93+a9bwU0AI51zh1R4C6FjUEv9IjCe/+C976N977N/vvvX5awSqRKFctMJ/ox6qJF0IGx5B5Y385Axrm0tDJWQOzJuefau9fixVZOVaa1IpNPOGGQLwExcaJVHRSz/SIsI8OG9m3bFqHg0tJg4EBLIr38coQeVKJu9mxrjSowgHLwYGt5jdj2UUYbNsBzIxwfnjGUcjlbdjl9ULWq5XDff19tGCIp4623di4HnW9ZqNL76y9L9Gv+gxSqfHkb+rBihR3d59Gx485F4BYujF1IW7daIeqRR1o3wv/9n+0vNmsWuxiuv94WCrnnHssLRt1nn9nsuQMPtMqHgw+OwZOWzWuv2bmTeJwVUlwRWQXDe/8XMAE4q8BNS4GD8vy7ARB3h/rhpTgT2dJ5W/gbn1CuY4f4W/6uEOnp9scT1WKXU06xd87Vqy0JEZFBBckhK8vmfR6RN2U4apRVPpxV8M949zIy7CX+/vsIBtizJ5x6Ktx2G8yfH8EHlqiZONEuCyQgPv4YPvnEdmTiwb//bd1aV9zfzM54vvqqDbnN45JLrHtLq2GIpIBNm+Duu+1U71VXRexhv/vOLlUBIUU66ijo0cMSEQWWIR82zAZT3nprbIpBv/3WZpXcdx9ceKENir/++tgfUlSqBP3729/Pu+9G+ck++MDOODRtasmHgw7a888EbNs2O7HTps3ONuhEVJZVMPZ3zu0b+r4qcDpQ8AhvDHBNaDWMtsAa7/2yUkcbJcmQgNhr6pdUZz3lOsV3+0VYiQdRltaxx9o0m9xcS0KE9whSmPc2syEjI88Hi/eWgDjtNCtBK4FTT7XLiM2BAPvUfeUVO0NwzTXxc/pcijZxoo2wLnD2IHz2pm9fS1QFads2m/113HG2jBh9+1rJ9a235tvGzj7bymDVhiGSAoYNs2rJIUP2uPpTSWRn26USELJbDz5oByI335yv7O6gg+ymzEyryIuWNWugWzc48UQrYszMtEXogpxlf8UVtuLGffdFsRLx7bct05Kebmcb4nh4f17vvWernffpkxDnm4tUlnfausB459wMYAo2A2Ksc66Lcy48V/xDYD7wC/Ai0LVM0UZJMiQgms4dy5ZyVRImHZaebpdRT0CAZTu++srKKk891Xq9UticOVZ9kq/9YvZsqxUtYfsF2N/P4YdHOAEBNoXp2Wft/+vRRyP84BJxkyZZ9UOeT8Rt22y//txzLflw330Bxoed7PjlF5sy7hyWZRgyxBKTL764435aDUMkRaxYYcMAO3WK+HJ706bZQWQMu4olEVWvbkmw77+3yzx69LB2iB49YN26yD/1qFF2oD9ihBWczp4N55wT+ecpqfLlrRXkhx9sLFjEvfyyZTnatbMWjASZEus9DBpkq16UYnc9rpRlFYwZ3vvW3vt07/0R3vsHQ9eP8N6PCH3vvffdvPdNvfdp3vupkQo8kurVs2VmYtJrFA3ec+zyD5hT97SI9S5GW5069qEc1TkQeTVrZkmIAw6wXq9PP43RE8efQuc/jBpll6Vc1Dkjw6rXIj6x+YorbL3s++/fuZ6ZxJ9Vq+DHH3cZQPnrr3YAf955Nm9r+PAYJR2LMGSIzam98MI8V15yiSUm+/Wz5S/yXK02DJEk17+/DYaJQpJbAyil2C64wFoB7rvPsvYhFSrYage//RbZBP5vv9nn4AUXWOHipElWHVitWuSeo6wuuMDmpzzwgM2miJhnnrHektNPh3HjSlz1G6TPPrP3lbvusiRNIotcrVkCq1fPztQl6qp/ubN+oGHuAhanJUb7RVh6egwTEGBn1L/6Cg45xN7owwfdKSYry1avaNIkz5WjR9vBY93SrZKbkWH7cFOmRCbGHZyzo9Y6daw3d9OmCD+BRMSkSXZZYP5DePZr48Z2NmPffe1MThCLm0yebOuq9+xZYOlZ5+Dpp61EY+jQHVerDSNxOOcOcs6Nd87Ndc7Nds7dVsh9TnHOrXHOTQ99BVyPI4H74Qc7uuvSxcr4ImjdOvjpJw2glGIKfw5t324fknkcd5xtosOGlf08zPbtVu3QvLnNaR80CKZOtcU44o1zNhN24UL4178i9KCPPgrdu9tZkTFjEuakbdigQVC/fkRH1QRGCQh2HnMti7vpFMWz/q2xdnnyuQFHUjJpaVbuFdP2/jp17JRm69Zw8cVRqu2KX7m59uvnq35YssQ+gcpQzxWuXI14GwZYadzLL9tEpEQe+ZvMJk2y3ukCezHh+Q9Nmth/44ABtv0FcVA/ZAjssw/84x+F3NiypZVgjxixI8mlNoyEkgv08t43B9oC3ZxzLQq531fe+1ahrwdjG6LEnbvusizj/fdH/KGnT7dEqyogpNgaN7aKnNGj7SuPRx6xquEuXUq/zzxnDpx0Etxyi31Uz5xpi0BFe2nNsvjb32x820MPwcaNZXgg7219zz594PLLbSekcuWIxRkLkyfbPvYddyRc6IVSAgKrgIDEnQPhMscynSOp3Tr+p7fmlZ5u+/rz5sX4iWvWtBaMk06yAYcjRsQ4gOBkZ8PatQUSEGPG2GUZEhC1akGrVlFKQACccYadFRg2LKXbZ+LWxImWUSxQv7lggZ3FCA+WvvFG207uvNMqZmJl4UIYORJuusnabQt1++3Wc5EnKRluw/jii5iEKaXkvV/mvZ8W+n4dMBeoH2xUEtfGj7ehMH37RmVIQ3gApSogpERuv92WJ+ve3SZChuy7r7VITJlS8l3WLVssr9GqlZ3HeeUV24065JCIRh4V4SqI33+3kWCl4r0dtT/8MNxwg61hGc9ZlyIMGmSHLzfeGHQkkaEEBAmegFi1iuozv2EsHWjUKOhgSiZmK2EUpnp1+PBDm453yy22pk0KCM9/yDerdNQoKz897LAyPXZGhi3jFLUuiUGDrG7wuuts5oDEh23bbPmwAu0XYAf+9evvzNaXL29VpkuWxHau6FNPWYFGgcrW/E46ySqjhg7d0SMSbsN4553YxCll55xrDLQG/lfIze2cc9875z5yzrUs4udvcs5Ndc5NXZGofZmye9u3Q69eNhDmtl26dSJi2jSrrj3wwKg8vCSrihWtLWjJEssa5NG5s40t6Nu3+BXbX39tiYcHHoBLL7UExLXXJtbqCe3b2+rwgwbZih0lsm2brS4ydKj9rb/wQkIOT5g711ZCufXW3ZxESTBKQLDzAyIhExAff0y57dsYSwcaNgw6mJJp0cIOCmI6ByKvKlWsvrpzZ6tDu+eeYJrTYygryxI/BxwQumL1aquJP++8Mj92RoZl2idOLPNDFa5qVTs7vXy5JY2S/P8qYcydaw3PhSQgFiywqtK8TjzRZosOHgzz50c/vL/+sv7Ryy6zFTeL5JydfZozZ0eVTdWqNi5GbRiJwTlXDXgX6Om9X1vg5mlAI+/9kcDTQKFDgLz3L3jv23jv2+yv5QuS0+uv28o3AwfafkAUZGer+kFK6fjj7TT30KG2MkZIeCTWli32UbU7f/1l7Rrt28PmzTbv4fXX8+z7JZiHH7bzTk8+WYIfys21bMuLL9qQ6SefTKzMSx6PPWb7I927Bx1J5CgBAVSqZBV4CZmAGDuWtVX2Z0GtYxJtlgp77WUlYEFOxadiRXtXvuEGq/O67bYEXg5l9zZtshUt87VffPihvUlHYD2fk06yxHLU2jDAGmoffNBOSb/xRhSfSIotnHEqsAIGWAVEvmGnIYMH2yDIXr2iGxrYvsf69cV8rssus4x0nr0ctWEkBudcRSz58B/v/XsFb/fer/Xerw99/yFQ0TlXO8ZhStA2brRTyMccY3/vUbBhg8231PwHKbVBg2xwUpcu+fZJmzWzzfftt+Hjj3f9Me/h3Xf/n737Do+iatsAfh+SEHovEopKUYq0V5AiIAkqkIAoQtBXsSIioBRFAbGhICJdBQuK+r1YEAQEKdKLBCTZBKSDpNCr9E7O98edhRBSNrszO7O7z++6cg0km91D2OzOPOcp3OD76itWHmzaxAwCX3b33cCjj7KX09GjLnzDxYtM+Zg6lQ00PvjAZ4MPe/Zcv0zxp5i4BCDShIX5YADiyhVg/nysLRmFirf5XkoRwDfoVasYobVMUBDTsvr3Z37488/75XbnmjV8Tb4hADF7Ni+47rnH4/svXJh3Y2oAAmDjsHvvBXr1umFclbDI2rU8UapW7YZPX74M7N17cwYEwLKMIUNY/fPHH+Yt7fJltg0JD2d1RY7y5uXzasECZnZApmH4AqWUAvA1gK1a6zFZ3OaWtNtBKXUPeP5zLLPbmslP49u+Y+xYzgceM4YpmCbYsIH/z5IBIdxWogSvtteuZSQhnTfeAO64A+jZ88aS1717ObqyUyee1v31F++iYEEvr90kQ4e6ODH33Dlm9c6cyROAQYO8sj6zjB17vWrMn0gAIo1PBiBiYoB//8XvPtj/wal7d0Yzv//e4oUoBYwaxUK5b79ll1xDBw9bb8kSxlpatEj7hDMvr0MHw07EIiL4pnf6tCF3l7mgID5hrl5lep2c0VsrJobZDxl2F/bs4X9NZhkQAFNIq1Rh0tHly+Ysbdo0npTl6o27Rw+mZaeN5CxQgGUYM2b4ZVzSX9wLoCuAiHRjNiOVUj2UUj3SbtMJwCal1AYAEwA8prV367jef59BWqkes8jBg9xZ7tiRtWAmcY5KlAwI4ZEnn2T0fOBA4NCha58ODWUjyt27ubmfmsoGjTVrMqA/ciTPw/wtAFazJtC1K/Dpp9lcr50+zV2DP/4Avv7a52sWjh3j/uh//wufvc7LigQg0vhkAGLOHOiQEPx8/AGffWK2bMkXydGjbXAdqRTw9tvcGZk+nRfmHs39sZclS3jyW6RI2ieWLmVuugHlF04REYwLrFpl2F1mrnJldhZcvjyXRYHCUP/+y0yBLPo/AJlnQAA8iRo3jqnKn3xi/NK05utK9eo8H3FZqVI8y/n++2u5nlKGYW9a69Vaa6W1rpNuzOY8rfXnWuvP027zqda6lta6rta6sdZ6jbfXWaoUewNs3uztRxYAOG7zwgUGIUwUF8da+/Iyh0V4Qilg0iSeh/bvf8OXwsP5NvXRR4z/9+7N46ZNwIABLHH0R++8w42ADz7I5IvHj7NL55o1LNF97jmvr89on33GrI/XX7d6JcaTAESasDAGGH1qh2vuXFxueh8OnS/icw0onZTii+WOHdenQVquXz+mvC1cyMK5Uxl7mfmeEyeA2NgM5RezZrFuIjzcsMdp0oQXlqaXYQDAs88yeDJ4cNaNRC5c4K7Xtm1MZVywAPjpJ24fjBjBnYUePdiItE0bvoNXr878xXbtZKsyJ3/9xWMWEzCArDMgAP6IIyPZ7PvgQWOXtmIF+8z17+9Ggk/fvnzufPklAAYwChSQMgzhmY4d+Vz8+WerVxKANm9mN9pevW4qFzOaw8HsBx8tORd2cuedPE/54Qdg8eIbvjRqFE/hdu9mvHzhQu7N+LPKldmf86uvMjSxPnyY57IJCUxXfOwxy9ZolLNnWUHSvj0ns/obCUCkCQvjDvzhw1avxEX//ANs3YpDDdoB8O3UnEcf5S7pxx9bvZJ0unUDfvyR6eWtWjEPyoetWMHn97UARGoqIz5t216fkWiA/PnZwNkrAQileIFYvDhfoTMGEPLl44LKleP4ziZN+O99/HFO0Rg0iNkuM2fySvXff4GiRYG6ddmg7PffOSdeZC0mhv8PDRve9KXERFbLZDt5AkxguXCBcSQjjR7Nhk1du7rxzTVrAq1bM9fz0iUUKMCnmEzDEJ4oW5bnyNOmSWzT6wYMYPrfW2+Z+jDnzzPW4W/p78JCgwaxY3vPnjc0TCtThlPkdu3i+1ygBLyGDGGGx3vvpX1i717WFu/aBcydCzz0kKXrM8rXX/PSw8dbWGRJAhBpwsJ49JkyjN9/BwBsreL7AYjgYO5SrlnDD9vo0oVZAn//zRc3Hw5CLFnCa/FrG9Xr1jHlx8DyC6eICAahvfLjKl2aOwPFi98YQOjQgc0Fhg/n3KoffuDEjzVrOGZx/36mNV68yJ/D9u38mSxcyO3JmTP5hv/WWzaoDbKxtWsZmr9W13NdUhJQsWLOqaB33MGkoylT+F9ghG3beB7Sq5cHU/b69uWw9WnTALAM48gRYOVKY9YoAlN0NDP+LBs/HYgWLWK/oyFDgJIlTX2ov/9mGaL0fxCGyZePpRg7d3J0bDrlywPFilm0LouEhbHk5H//A3Yu3M1Zo/v38/ztgQesXp4hLl1ihkuLFpkmmPoHrbXtPu6++27tbevXaw1oPXu21x/aPQ88oHX16nrsWK776FGrF+SZM2e0LlFC64cftnolmViyhD/kkSOtXonbatbkU+aa11/XOiRE6xMnDH+sP//kj2vGDMPv2rumTuU/5KefrF6JPV29qnXRolp3757pl5s21bplS9fu6tQprcuV07phQ96tp7p31zpfPq0PH/bgTlJTta5RQ+v//Efr1FR99qzWBQpo/eKLnq/PEwBitQ3epwPhw4xzkSNHtA4K0nrQIMPvWmTmyhWta9fWunJlrS9cMP3hJk7k20ZSkukPJQLNf/+rdd68Wm/bZvVKLHfkiNb/KbBVH81fnhcP69dbvSRDffstX0fmzbN6Jdnz5HxEMiDS+FQGxOnTbL7Xrh2Sk1mbXKKE1YvyTMGC3K2cPZub0bYSEQHUqXMt68TXHDjATf9r5Rdac4c/PJwZAwZr2JD/n14pwzDTY49xd9/Z9UjcaNs24ORJlr1kIikp+/4P6RUuzM7d69cD333n2bKOHGE97FNPeTgzWylmQTgcwKpV16ZhSBmG8ESpUnwtljIML/nuO6YljBhhaLlhVhwOno/5al8uYWNjxjCVtWfPgH/xKLVvA1agBS6fv4LNE5cDDRpYvSTDpKayuWidOqws9lcSgEhTpgybQ/lEAGLRIs6ta9cOKSksv/CH2q/evXl+MHq01SvJRFQUsHo1uzn6GGcg4FoAYts2pvJ16GDK44WEMG3M5wMQefJw8PT27cz1Ezdau5bHTPIDL1zga2lWEzAy88QT7B8ycCDjGu6aOJGP36+f+/dxTdeuTNlOm7QiZRjCCNHRbOMUH2/1SvzcmTMsu2jSBOjUySsP6XCw/4M/nJMJmylbloG0pUuBqVOtXo111q0DWrZEgeKheKjoSrz2bW2rV2SoOXM4XGzgQP9+HZEARJrgYP5u+0QAYu5cFn01bYrkZN/u/5BemTLA009z9zLdyGN7iIpiYecff1i9klxbsoRPl/r10z4xaxaPJjbqiYjgC+iBA6Y9hHc8/DDPJt97j0V54rqYGD6x7rjjpi+lpPDoagYEwDfaTz7hBf615lK5dP48x1a1a8depB7Ln59TUmbPBnbvRmSkTMMQnnv4YZ5zyDQME+3cCTRrxvE6o0d75Uz+4kUmW0j/B2Ga7t2Zddi/P8dOBpoVKzhqs2RJ5Fm9Cp0G34EFC7g/6A+0ZpuP22/nhoc/kwBEOmFhPhCASE1lKcpMor0AACAASURBVECbNkBIiF8FIADg1Vd5nffJJ1avJIPGjZlX6WNlGFozABEezokEABiAaNgw5/EEHoiI4HHZMtMewjuU4sDppCS2JBbXxcTw9yKTGZeJiTzmJgMC4In7Cy/w93/Lltwv6X//YwAjw8h0z/TsyavFCROulWHMmCFlGMJ9JUvyHFrKMEzy8898Mdm7lxs2Xuritnkzk1MlACFMkycPx4gfP84t8kCyYAGvfSpWZBribbehd28OPRs82D9eS1euZILHgAE5N/D2dRKASMcnAhCxsZwV2q4dzp4Fjh71rwBEtWrcHZo4kdmTthEUxBe++fOZCeEj/vmHu9HXyi/27wf++suU6Rfp1a3LwRQ+X4YBcBxjs2YMRJw/b/Vq7OHkSUYIsjixT0riMTcZEE4ffAAUKsQhJrk5oUhNZYls/fpAy5a5f9wshYVxIs7XXwMnT0oZhjBEly78PYmNtXolfuTCBQYMH3uMBdTx8UBkpNcePi6ORxnBKUxVty77E331lc1Gx5lo5kxm7daowSyItMZ9BQqwymrVKp9MUL7JiBHMBn/mGatXYj4JQKTjEwGIuXMZAW3TBnv28FP+1uxowABOVLTdhnNUFK881q+3eiUuW7KEx2sBiN9+49HkAERQEC8C/SIA4cyC2L+fo7AEg1haZ9mAMjGRvUDKlcv9XZcuDbz/PrB48fVqIVfMn8/2Jq++akK2db9+jIh+/bWUYQhDdOjA35G0Ka/CU//8wyYykybxJGL5cu6UepHDwb7OlSt79WFFIHr3XT6/X3yRaTf+bOpU1iM0aMCTygzdpV94gdmWb77p21kQCQlM8ujbl9Wf/k4CEOmEhfH60tal3nPn8k22ZEkkJ/NT/pQBAXBTtVkz9n2zVZpzmzYM/vhQGcaSJXxe33ln2idmzWKaSY0apj92RAQvRJ3p+D7tvvs4X/rDD22WmmORtWt5ld+oUaZfTkri69K1sp9c6tGDA0j693c96WT0aFYVRUe795jZ+s9/2Fl1wgQUyHvl2jQMH0qGEjZTvDjw4INShmGI6dP5O5qUxCD7yJGM7nhZXByX4c+N44RNFCrEWsVNm1gXOGYMGyGcO2f1yoz15ZdsBt2iBVMcihW76SZ583JYWVwcEyV81UcfcSLYSy9ZvRLvkABEOs5RnAcPWruOLO3bx5TCdu0AwG8DEAA3MJKTbbbLWKIEoyM+EoBITWWwuFWrtBOikyf5iQ4dvHKG5OwD4RdZEAC35Y8eBcaPt3ol1ouJAWrWzHKMa2Ji7vs/pBcczHOrpCRg1Kicbx8fz34jr7xi4nVHv358UZo9G507sxJOyjCEJ7p0YYncunVWr8RHXbwIvPwyd0dr1OALQfv2lizl8mVg40bp/yC8qEMHbvtv387Uv+bNgSJFgHr12Kxy8mQ+KW21k5cLY8cyw6NtW553FyqU5U2ffJIbbW+95ZsbA7t2MRjds2emMRa/JAGIdJwBCNuWYTgvfNMFIIKCrq/bnzi72H/8sc12h6KieJJj2yfJdRs3AseOpSu/mD+fZ0kml1841ajByTJ+E4Bo1Igntx9/zBqhQJWaygyILMovAAYO3On/kF7Llsxm+PDD68HWrIwezXOTF17w7DGz1b49c6vHjr1WhiHp88ITDz3E3Tt5HrkhMZGpkp9+ylSplSst3Y3ZsoXxEOn/ILzK2ST74EHObxw8mF0Zp0/nG2LdutwoaNECeO01vtgkJdnsxDoDrfnv6t+f43NnzsyxJiE4mHtEW7YAP/zgpXUaaNQobp706WP1SrxHAhDp2D4AMXcutxVr1gTAnZMKFdxPc7azPHkY0I2Pt9kFbFQUj/PmWbsOF9zU/2H2bHa3yebC0UhKMQti6VJ7v9flyvvvM5Nk9GirV2KdnTsZgMmiAeXZs8wO8CQDwunjj3kcMCDr2+zdy6b33bqZvHMQFMQUiz//RIHN6xEVJWUYwjNFi7Kyb9o0xvWEi2bOZLfZnTv559GjGcmxkMPBo2RACEuULcudu6FD2Ujg2DH+fkydykDElSsM1nXpwt2BW25hUP3994GFC+0z0lNrYNAgpjI89RTw448u/24/+iiTP95917faYhw4AEyZwsaT7vTN8lUSgEjH1gGI8+fZla19+2vp8/42gjOjJ5/ka6rzIsQWatdm1McHyjCWLAHuuCNt2ubFi1zzQw95NWIVEcHA/LZtXntIc9Wty235cePYMCYQxcTwmEUgy5mtYEQAolIlnov88kvWI10nTODFm1d2Dp57jimuY8dKGYYwRJcurK50/lqJbFy6xFKojh3Zyyg+3msZfTmJi2MWVrVqVq9ECPA6oWpV4L//5fnKmjXAqVN8ok6axM20xEQ2T2jThrOBq1UDnniCZaYxMd6f+pWaypKqjz5iI4QpU3I1izJPHmDYMGD3buCbb0xcp8HGj2d86LXXrF6Jd0kAIp1Spfhct2UAYtkyvhiklV8A/h+AyJePG44LFwIbNli9mjRK8f9g0SJe1NvUpUu8MLqW/bB8OXD6tNdP1vyuDwQAvPcefxdHjLB6JdaIieHWbRaNTD0ZwZmZAQN4X6+8cnMp6+nT7FHVqZMxAY8cFS7MVItffkFU3b0yDUN4rH17IDRUyjBylJTEGvdx4/hisHq1cS8yBnA4mJSRR86qhV3lzcsUnR49eIW+aRNw4gRP0EaM4OjalSs5hqFpUwbb7777ejBg82bzUv6uXgWefx747DNeiX/2mVu/TG3bculDh/rG1PSTJxkP6tyZ8aJAIi+V6eTJw/QXWwYg5swBChZkN37wRHzfPv8OQAB83StY0LVGdF4TFcU8cxtvff71F5d4LQAxaxZ/kNc+4R23387nqF8FIKpXZ1fmiRP5Sxho1q5lP4wsTg6cU0+MCgjky8cG35s23TwF9euv+Qb+6qvGPJZLXn4ZSE1FgSmfISoKmDFDyjCE+woXBiIjGciS51EWfvuNV/fbtrG2ffx4Rm1s4soVjtCT/g/C5xQpAoSHA2+8wTezPXt4XjNrFvD662y+/uOPzP676y7WOWa8vac1tpcvM1Pj229ZPzFypNuN0pUChg/3nanpkyYxMeWNN6xeifdJACKDsDAbBiC0Zv+HBx+89qa7bx9PVipVsnhtJitenOVrP/3E1zlbiIjgVZGNyzCWLuULcXg4mNY2ezbT7PLl8+o6nH0gli3zsxrnd97hWeewYVavxLtOn2YkIIcGlPnyscTUKB06cArq229fr3y5coWboc2aAffcY9xj5ei224BHHgG++AKPtT8rZRjCY9HRrAP+80+rV2Izly9zN7RDBzaAdThY6G0z27dzt1X6Pwi/EBbG37lhw5jte/w4g3/ff89GBefO8c23UydehGS8fW6adF+4wN/padO40/jOOx5PaUs/Nf30aY/uylTnz/PH2Lo146uBRgIQGdgyALFxIzutpSu/SEnh0d8zIABmg2nNX1RbKFCAV/Y2DkAsWcIXtBIlAKxfz7Nbi2plIyL4fmSbMhoj3H47U/EnT76+5R8I1q9nJCmLBpQAfxy33mrspFeluOl55gynjgFsAJmc7OXsB6d+/YB//0XUse+RP7+UYQjPtGvHJu9ShpHOnj28khg9mrPp/vwTqFLF6lVlKi6OR8mAEH4pTx7OuOzalfOx163jtv26dfz7Aw8wCjdkCDdKS5S4+fYXLtx8v2fP8sVvzhymAhj4Zj5sGKem2+a6IRPffQccOgQMHGj1SqwhAYgMbBmAmDuXx8jIa59yNnoLhADErbeyUdeXX7JczRaioji4d8cOq1dyk7NnWaZ/w/SLoKDrEzy8LDycR78qwwD4ZpsnD4sNA4WzU16jRlnexIgRnJmpUYOl35Mn84R/9GjWTLZvb/xj5ahpU6BhQ4ROGof2UakyDUN4pFAhvjxPny7PIwCcMlWvHrOtfvqJ9eBezt7LDYeD+xJ33mn1SoTwktBQph727s3MiG3buNO0aBGv/mvUYOP8V15hxmSRIkDDhkCvXrzydji49b9sGf/eo4ehy2vYkHtuo0bZZ8BHeleusMF+o0bXKusDjgQgMggL4++QrZqXzJ3L36Z0Oc3OAIS/l2A4DRjA3c/PP7d6JWmcF/M2zIJYvZqZqzf0f2jZkvUsFihfnidmfheAKF+eO3Pff8/ofyCIiWEPjGyeS4mJ5jWEfPttTpJ99FH2OenXz6IxxErxwXfsQO+q83HoELBqlQXrEH4jOpq7YQFdznP5MrcDo6J4chMXx90Hm4uLY7zEH0eiC+GyYsWA++8HBg/meef+/cxkmjED6N+fDW+cZRx338038WnTOG7TBO+/zxKMkSNNuXuPTJ/OaR0DBxqbLepLJACRgXMU54ED1q7jmsOHmb6UrvwCYACiTBmmbQaCevX4ujZ+vE2GT9x2G1Czpi0DEEuWACEhrI3H9u3A1q2sz7NQq1Y8sfal2cwuGTiQv4Tvvmv1SsynNRtQZlN+ceoUdxvMak5ftCibdScnM8vzmWfMeRyXdOoElC+PpmvHSvq88FhUFHfRA/Z5tHcv0+U++gh48UUGO31gpmVqKqeBSv8HITJQinPgO3bkG/fSpUxj3ryZDSfXrDG1p8tdd7G35YQJNrqmA0+lRoxgkshDD1m9GutIACIDZwDCNmUY8+fz2ZohAJGSEjjZD06vvw4cPAhMnWr1StJERfGq2mZdbpYs4TViwYJg+QVgeQAiIoIZLLGxli7DeGXKAH36ME1440arV2OuXbuAY8dybEAJmDsS86mnuCk6dCgv2CwTEgL07o2g5UvwUrO/pQxDeKRAAZYTzZhx87hZv7dgAZsWJSQAP/zAVEcbl1ykt3Mnyx6l/4MQLggK4ubd008DDRqY/nDvvsux9MOHm/5QLluwgD3R3ngjsMf2BvA/PXO2C0DMnctFZWiRmpwcGP0f0rv/fmZCjBplk4kKUVHc0l+0yOqVXHP8OHdjbii/+M9/LI9WtWzJo9+VYQDs0l60KOsD/Jmz/0M2GRDOAIRZGRAA37B/+omlpJbr3h0oUAC9royTMgzhsehoTnlZvtzqlXjJlSvsKtu2LWegx8UBjz9u9apyxdmAUjIghLCfqlWB558Hvvjieum61UaMACpW9LmXOsNJACIDWwUgLl0CFi7khW66IiGtAzMAoRSv9bZuZY8qyzVtygtPG5VhLFvG50erVmC6yNq1lk2/SK9kSQaP/DIAUbw4n5izZ7Om0V+tXcsazpo1s7yJcyCImRkQtlKiBPD007h9zVRUyndYpmEIj7Rty4aUAVGGsX8/dxWGD+cVwtq1PtnF0eFgP74aNaxeiRAiM2+9xY2L996zeiWsOlm5kgM/8ua1ejXWkgBEBsWL883EFvVCq1YxvT9D+cXRo2ySGWgBCIA7RBUrsnus5UJC2MV33jybpGSw/KJQITYnxpw5jEbYIAABsAzjzz8zn8bk8/r0AUqV4judv4qJ4RMrm05rSUks/SlVynvLslyfPlAXL2Jk5UmYMUPKMIT78udnTfCvv/phv5z0Fi1iRHr9ejalmzzZ4noq98XFAXXr8nRACGE/FSqwX/h331nfL/yjj7hv0a2bteuwAwlAZKAUMwFtkQExdy6jIdfy6SklhcdA6wEB8E2+Xz9GENets3o1YHbKwYOse7CBJUuAFi3SToZmzQIqV2YnHhuIiGADUWcmvxG0Zu8yhwM4d864+821woXZkPKPP/yzjf3Zs+xxkU35BXB9AkZAdXW+804gKgod9k3EiUMXpAxDeCQ6mq1W/DJb7OpVlqq1bs3+ObGxQNeuVq/KbampfO+R8gsh7M3ZL9zKStnNm4HffuNk0oIFrVuHXUgAIhNhYTYIQGjNHeyIiJueqc46pkDMgAAYOSxa1CZZEG3b8mrLBmUYe/cCO3akxatOn+YM5g4dbHM12Lw5N8/dObG+coX/ttmzWT/39NPcjC9alBkxd9/NMdN16zKbd+JEVkN4NduiZ09GL4cM4e+vP1m/nmfbOQQgkpLM7f9gW337It/Jw3gq5CcpwxAead2ar2V+V4Zx8CDwwAOcjff009xB8PG6hd27OflHGlAKYW9lygB9+/J1NSHBmjWMHMlEr969rXl8u5EARCZsEYDYsQP455+byi8ACUAULsxrvV9/ZWN+S5UuzSthGwQglizhsVUrsM3upUu2Kb8AeFLdsGH2AYhz55hM8sMPrGbo1IkJHAULcqP54YeBQYMYWylShOexn30G/PwzI9zlyjHC3KsX0KgRnyv16wMvvMAmRLGxJo5xzZ+fwYdVq5gJ4U+caSuNGmV5E62vZ0AEnFatgNq1MbjAWMyYrqUMQ7gtXz7GjWfO5Eu4X1i6lCUXa9cCU6bwww+2AB0OHiUDQgj7e+01oFgxnqZ5W3Iyz2u7d2dPNAEEW70AOwoLs8H1w9y5PEZF3fSl5GTW+Rcv7uU12cjLLwOjRwNjxnC321JRUczrOnQIKFvWsmUsW8ba+9q1AYycxb80bWrZejITEcEocEoKsGcPG4qm/0hOvp48kCcPUKUKN8nateOxRg0GIooWvfm+o6N51Jr3HxvL+tzYWI62mzyZXw8JAerU4QSou+/msVYtgxoCdevGf+CQIcCDD9om+8Rja9cCd9yR7TvniRPcDQzIDAilgL59cdvzz6MmlmH16gjcd5/VixK+Kjoa+L//Y6A1MtLq1Xjg6lVg2DDOwrvzTv6DbFISaIS4OL6f+NE/SQi/VawY8PrrwODB3FPJIaHTUGPG8Ni/v/ce0+6UtmGqcIMGDXRsbKxlj//RR9xNPX2aF/qWCA9nIejGjTd9qWNHNlLZvNmCddlIt27A1Km82Cxd2sKFOBy8kp0yBXjmGcuWUbs2s2LmzrzMH0jHjsA331i2nswsWcLG5+nlywdUr84PZ5ChRg2gWjW2QDGC1iwPSB+UiI0FTp7k10NDrwclnIGJWrWAYHdCtFOmAM89xy1MG2WguE1rBtbatmUXpyw4fw1mzOBTL+BcuABd6VbMP3YPfu8xB599Zs7DKKXitNbmD1AXlp2LXLzIX7mHHwa+/dbrD2+Mw4eBJ55g0OHJJ4FJkyw8oTLHAw9w9LVzFKcQwt7OnmVrtJo1mZjljT2iI0d4bt6lC08P/Ykn5yNSgpEJ5yhOyyZhnDjBNO5Myi+AwBzBmZlXX2WN/6efWryQ+vWZ+29hGcb588wgqFcPwIoVvLK24cXvffdxFNKoUfxx7d7NN4T4eODHH5lI0rkzd5SMCj4AfJO5/Xbe94gRPCf+91+W8Pz0EzNqChViQOv55/lzrFqVP8pc69qV2QJvvWWb6Sge2b2b76Au9H8AAjQDAgDy5YPq+RIiU+ci/ucdUoYh3BYaCjzyCPsIm1YyZqYVK/giuno1U8++/97vgg9aM/Ag/R+E8B0FCwJvvgksX369bNlsn3zCa5XXX/fO4/kKCUBkwhmAsKwPxMKFTF1s3z7TL0sAgmrU4I/os88snoCgFPNk//jDstlpmzfzKVO/PnjWmj//zakGNhAczCDDq6/yR3b77Sy1sIJSLPHo0oUNTZcuZVBi+3amP4eEMBHp9ddzeREQHMwoy6ZNbE7h69au5dGFCRhAgPaAcHrpJVwNzosnjo3H6tVWL0b4suhoxpEXLbJ6JbmQmsqSi4gINuBZt44RXX8pRUsnOZnvF9L/QQjf8uKLbF7+5pvm9ws/fZqbpA8/7PM9dw0nAYhMWB6AmDOH9fv33HPTl86eZWWGBCBowAD+PCxPa4qKYgG8RVcdzimg9epqBiBat/bZuepWypOHyQtPPsmf6QsvMDjRqBHjCS6LjmZNzDvvcISHL4uJ4bZBrVrZ3iwpib05Ark3DcqWRepj/8Uz+BZz/+9fq1cjfFirVvxd8pkY5pEjjCoPGcLXv9hY1rX5KWfZhWRACOFbQkN5avbXX2xabqavvmKg8o03zH0cXyQBiExYGoC4cgWYP59v5EFBN3050CdgZNSsGdC4MRu8WJryfP/93DK3qAwjPp5TIW4/Hgfs22fL8gtfU6gQJ2fMmcNyrLvv5vPMpaqKPHk4bm7nTqYf+7K1axkMzaEhRsBOwMgg5LW+KIhzKPLzV1KGIdyWNy97qcye7eVxwu5YtYolF8uXA59/znbvhQtbvSpTORx8Saxd2+qVCCFy6+mn2WdsyBDzKmUvXmSz/PDwbAeIBSwJQGSiSBFuHlsSgFi7ll2Nsuj/kJLCY6VKXlyTjSnFLIjduzmW0zKFCwMtW1oWgEhI4PlfnjmzefGbxfNH5F67dsDffwNt2rB05P77OcEjRw89xI6W773no4XcYG3Thg0utYtOSgrg/g/p1a2LQ7Ui8MyZT/DncmtKsoR/iI5mCu/ChVavJAupqezaHR7Ok6aYGOY3+2HJRUZxcUwKy5fP6pUIIXIrOBgYOpSZrT/9ZM5jTJ3K68iBA825f18nAYhMKMUsCEsCEHPn8jfjwQcz/bJkQNysQwc2DBw50vx6rmxFRQHbtjEa4kVXr/IasV49sPyiRQsZNGywMmX4o508mWl7tWtzky9bSgEffMCooXMGqK+JjWVWVuPG2d5Ma8mASK/IO/1QEXvxz8gZVi9F+LDwcL6U27IM49gxNmEaOJCpGnFxaU2I/J/WzICQ/g9C+K7oaFaJvfOO8e3brl7lNUn9+pyWI27mdgBCKVVRKbVMKbVVKbVZKdUnk9u0VEqdVEolpH287dlyvcfSAESLFiymzkRyMuMT5cp5eV02FhTEnenYWDenFhglKopHL2dB7NrFjeoWYbsYzu3QwauPHyiUYj+1DRu48/XEE8Djj7O+L0sPPgg0b85AhKWdUt3kbECZQwDi6FH+8yQDgvI/Gon9haqh7rKxuHrFfqOuhW8ICeG1/W+/cdKRbaxZw4j34sXsAv3zz0wdDRB797LlhfR/EMJ35cnDU7Ndu7KdMO6W2bPZ0HzgwIBICHOLJxkQVwC8qrWuAaAxgF5KqZqZ3G6V1rpe2sdQDx7PqywJQCQmcpxBNunzycns3ppJe4iA9vTTQOnSbBhomapV2cHQywEIZwPKxodm8w8SgDBVlSoMdH3wATB9OrMhFi/O4sbOLIiDB4GJE726TkPExPAfXLp0tjeTCRgZ5MmDA5374D+X/0LyvM1Wr0bYxdKlwLhxwJdfMj935kzWV6xaxS31bduYMXX0KCMOWqNLFzafnj/f6sWDW/+jRnGeckgIAxE9ewbcGbbDwaNkQAjh29q1Y3+G994zrteO1hz3XqUK8OijxtynP8q+q1g2tNYHABxI+/NppdRWAOUBbDFobZZyBiC09uJ7q/PCNZsAREqK9H/ITP78QO/eTKXavDnHhv3miYrihebZs5wc4AXx8UCR4HMImzEBaNhQtqG9IDiYI5zatOHEjAceAPr0AT78kM/FG7RowUyIESNYH+0rzdm0ZgDChfzBpCQe5al33V2jnsH5F5ujcqO7rF6KsItff2XGQC5EFCiAI6oAUrsWBIYUYK+FAgX4/lIgi79n97WMf8+Xz7WTnOPHgWeeYVfejh2Br78GihVz7+fg4xwO7p7WrWv1SoQQnlAKGD6cU4e++ILncZ5atgxYv573J5vFWXM7AJGeUuo2APUBrMvky02UUhsA7AfwmtY60+0gpVR3AN0BoJINrrDDwphSfOpUltUQxps7lzvo1apleZPkZNaFipv16sV+WKNGWTiWMyoKGDsWWLKETQi9ICEBGFXyQ6iUFOD//s8rjyno7rtZ+vzGG8D48cCiRcD//pdJKfT77zPMPno08O67Viw195KTgUOHXG5ACUhvmvRCSxQEGvnvGELhhjFj+Fpw7hw/zp69/ueMf0/7szp3Dpvnn8Pe7WfR5c5zCL6U9vVjx9gNN+P35ralu1I3BieyClisWMFxQOPHAy+/HHBZD+nFxQE1asikayH8QUQEP4YNY5ltoUKe3d+HHwK33AI89ZQx6/NXHgcglFKFAMwA0FdrfSrDlx0AbtVan1FKRQKYBSDTq2ut9ZcAvgSABg0aWF40m34Up1cCEGfOMGzWu3eWN7l8mRMW5SQ/cyVLAs89x6jjBx8A5ctbsIjmzbnD/fvvXglAaA2ciN2FZ06MZFOCFi1Mf0xxowIFgE8+YeLSs88yzjB0KKezXIt+33MPOx6NGMH/p2yCjLYRE8NjDv0fAJZglCgRUGXgQuRe3rz8KF48V9+m2wFPhgN5/wt07pzdDTVw6VL2gY3c/P3YMaZdnjvHN9hffuFrWYBzODgNSQjhH4YN417LhAnA4MHu309sLEtyR46UCTk58SgAoZQKAYMPU7XWNw1BTB+Q0FrPU0pNVEqV0lof9eRxvSF9AKJGDS884OLFPHHIpvxi3z5ubkgAImv9+7MCont37kTn8jzPc3nzMmV93jyv1O8c2K/x9vE+0KGhFjfAEK1bc1xnjx7AoEGMQX3/fbqyhLFjgQULeIPFi+2/g7h2LaMrdXLexZcRnEKYp3lzoGxZYNq0HAIQSgGhofwoUcJr6wskBw7wQxpQCuE/GjfmUJ+RI4GXXnL/2uGjj7hp/eKLxq7PH3kyBUMB+BrAVq31mCxuc0va7aCUuift8Y65+5jelD4A4RVz53L7sFmzLG+SksKjDSpUbOv229nj648/2KR7zRoLFhEVxTbZGzea/lD7vpiDKMzD3m7vymgUGyhZkhcJ33/P//66dYFvv00bDxsWxgyIpUt9o1QmJoY9RYJzjlPLCE4hzBMUBHTqxKDmmTNWryawSQNKIfzT++8DJ0+yjNsdO3YAM2awHFyyQXPmyRSMewF0BRCRbsxmpFKqh1KqR9ptOgHYlNYDYgKAx7TWlpdXuMJ5LeeVAMSFC5yz1aYNO0tnITmZR8mAyN7LLwOrV/OkrUULplZdverFBURG8mj2NIzz51Htkz7YhFoo9d7L5j6WcJlSQNeuDEDUr8+yjE6d2NgeL77IPL/+/dM+YVPnoAOqiQAAIABJREFUz7O7qQvlF1rztUkyIIQwT5cu/LWcO9fqlQS2uDi+xterZ/VKhBBGqlsXeOwxbmIeOpT77//4YyafvfKK8WvzR24HILTWq7XWSmtdJ92YzXla68+11p+n3eZTrXUtrXVdrXVjrbUV+9FuKVSIESyvBCC+/ZZDpXPI2XEGICQDImeNGvH6KToaGDKEVRH79nnpwW+5hfmZZgcgPvoIxU4k4cOwT1GkZNaBK2GNW29lssPIkWwcX7s2sHpNHjYpOXmSTSLsyuEArlxxqQHlwYOMoUoGhBDmufdeboxMm2b1SgKbw8Fe4b4yzEgI4br33gMuXmQjydzYtw/47jv2oStb1py1+RtPMiD8nnMUp6kuX2bRUKNGOY63SE7mE1sam7imaFGOWv/mG2DdOkY3vbZ7FBXFGvpjJlUc7d4NjBiBOQUfw6WmLc15DOGxoCDGGdavZ1CzSxfgZKXawGuvMfC4bJnVS8xcLhpQyghOIcyXJw/7P8ybx+lcwhpxcdL/QQh/dccdnDY8adL1sndXjBvHHn2vvWba0vyOBCCy4ZUAxE8/8Qz+zTdzbEqXnCzlF7mlFFPg4+KAChXYZKZPH+7Ymioqiq9GCxaYc/99+0KHhKDH2VE3j3wUtlO3LoNhBw+yQSXefhuoXJlZT6Y/Gd0QE8OIgguh/MREHiUDQghzRUdzd27OHKtXEpgOH2Z7J+n/IIT/evttHt9/37Xb//sv8Pnn3GCSjRjXSQAiG6YHIFJTmedTuzYvWHOQkiLlF+6qXp0JCa+8wjE7TZoA27eb+IANGgBlyphThvH778CcOUh88m3sR3mpRfUR99zD59+kScCfjvx8x9q5M/e5fmbTmgEIF8ovgOsZEBKAEMJcTZowkC5lGNZwNqCUDAgh/FelShxWNmUKT9FyMnEimwO/8Yb5a/MnEoDIhjMAYVrbzFmzgK1bOXQ2T/b/FVozACEZEO7Llw8YP579Pvfs4S7GlCkm/f/myQO0bQvMn89aeqNcuMCr2OrV8XvVPgAgGRA+5P33+TvcrRtwscUDwBNPMACxdavVS7tuzx7OmXOh/AJgBkSZMpzYKYQwj7MMY8EC4MQJq1cTeJwBCHnPFcK/DR7MhpLvvJP97c6dY/lFZKRLE8tFOhKAyEZYGHDpEnD8uAl3rjXHM1StmsNgbzpyhB2wJQDhufbtgQ0buCP93HO8Bjx50oQHioriWaKznt4IH3/M/g+ffgrHprwoW1amb/qSQoWY+LBtGzB8OIAxY/jJF19kRpQdrF3LYy4yICTtUAjviI7meclvv1m9ksATF8dTtqJFrV6JEMJMZctyr++nnzjRLCvffMOBZgMHem9t/kICENkIC+PRlDKMP/5gOH3gQHaqy4GM4DRW+fLA4sXABx8wnbV+feCvvwx+kAcfBIKDjSvDSEriVWvnzkCrVoiPl1FgvqhNm+uJD5uPlGFQadUqvpPZQUwM04Xq1nXp5omJUn4hhLc0asQUYSnD8D6HQ/o/CBEoBgzgNMS33sr865cvA6NGcUJR8+beXZs/kABENkwNQAwbxmLOrl1durmzG6v0gDBOUBB7f65cCVy9yheRkSMN3IguWhRo1sy4AES/fszBHT0aFy8CmzdLKqivGjuWb2wvvACkPvMc0KIF3+3cGT5ttJgY9jAJyXm069WrfG2SDAghvEMpZkH88QebnwnvOHaMewDS/0GIwFCiBKda/PYbJ+ll9PPP3ByW7Af3SAAiG6YFIFat4seAAUDevC59i2RAmKdpUyAhAejQgU1k2rThtAJDREUBmzblbp5PZhYsYM+Qt94CKlbEli1sLSEZEL6pdGkGIWJigEmfK+CLL4CzZ4H+/a1d2MWLQHy8y+UX+/dzF0AyIITwnuho/t7NmmX1SgJHfDyPkgEhRODo04fna0OG3Pj51FRgxAjgrrvY/0HkngQgsuGsrTc8ADF8OJ/R3bq5/C3JyUDhwkCxYgavRQAAihcHfvmF14GrVjH7fOFCA+7YOd3EkyyIixeBl1/mgOK0C1TnyZBkQPiuJ59klc7AgcCegtU5n/OHHwx64rnJ4WCBuYsNKJ0TMCQDQgjvadCAv3NShuE9zgaUEoAQInAULsxTs8WLgWXLrn9+3jxmIb/xRo4zBEQW5MeWjXz5mIJjaADC4eBudr9+uWobn5zM7AelDFyLuIFSQPfuQGwsu/q3acMklUuXPLjT6tV5puhJAGL0aGDXLuCTT65lzCQkAAULsiGW8E1KsSFlairQsyegBw5ikOmll9ha2dv27uUvQGgoS4dckJjIo2RACOE9zjKMxYtZGiDMFxfH17kSJaxeiRDCm156iRXzb755fWreiBG8JuvSxdq1+TIJQOTAOYrTMMOHszdAz565+raUFOn/4C21arEh5UsvXW8ws2uXm3emFNCuHbB0KceY5FZKCjtlduzI7fI08fHM0pDIq2+7/XaO5pw7F/hlTj6m4CQm8pPetGULa5GSkxksK1PGpW9zZkBIaZiwklKqolJqmVJqq1Jqs1KqTya3UUqpCUqpXUqpjUopn97Ljo5mGd7MmVavJDA4HNL/QYhAlC8fq59jYnh6tHo18Oef7A/hQqsskQW5fMlBuXLAgQMG3dnWrcCvvwK9e+d6jpMzA0J4R/78wMSJwIwZDD7Ur89WDm6JimLwIX3+lqucPQHGjr32qdRUZkBI+YV/eOUVplS//DJwvE5L4NlnGfn6+2/vLODPP5nxcPkyO7K2auXytyYmMkgbGmri+oTI2RUAr2qtawBoDKCXUqpmhtu0BVAt7aM7gEneXaKx6tcHqlSRMgxvOHmS5wFSfiFEYHr2Wb7eDhnCfeRSpYDnnrN6Vb5NAhA5MDQDYsQIXtn2uWlzJltnzgDHj0sAwgodOzLb4Px5drx1y333sdwmt2UYixYxAvLmmzekv+zezeeENKD0D8HBwOTJTKUeMAAcy1msGMshDBvJkoVZs4D77+e76Zo1uX5SJSVJ/wdhPa31Aa21I+3PpwFsBVA+w806APhe01oAxZRS5by8VMMoxfTfpUuBI0esXo1/kwaUQgS2kBDgvfeADRuA+fN5GZeLKnqRCQlA5CAsjBkQHl8HJCUBU6fyoqJ06Vx9q0zAsNZttwENG/JEzy358vEi7/ffrxeQ5eTSJW6JV63KPK90pAGl/6lbl8GHb74Blm4oCYwZA6xdy5IMs3z+OfDoo3zwNWvciiQkJkr/B2EvSqnbANQHkHFwWnkAe9L9fS9uDlJAKdVdKRWrlIo9YvMr++hojsL99VerV+Lf4uJ4lACEEIHrscdYol2oENCrl9Wr8X0SgMhBWBjrLI8e9fCORo5kwf6rr+b6W50THCUAYZ3wcPaFOHPGzTuIimIkacsW124/bhywfTswYcJN+e0JCdw1r1XLzbUIW3r7bcabuncHzj/6JEshBg40fgyP1nywl15ip9UlS5gBkUtXrrBvpWRACLtQShUCMANAX631qYxfzuRbbooIa62/1Fo30Fo3KJ3LzQJvq1OHfWulDMNcDgeb0LnYGkcI4YeCgpg0umgRJ+cJz0gAIgdhYTx6dA1w4AC3Np95hu9iueTMgJAmlNaJiOAF1+rVbt6Bc1CwK2UYe/cCQ4cCHToAbdve9OX4eKBGDSZWCP+RPz/w5ZfAP/8A7w1VwKRJHMGay5KtbF25ArzwAptcPvccMHs2x6m4Yc8e7r5KBoSwA6VUCBh8mKq1ziwnYC+Aiun+XgGA0UO2vcpZhrF8OXDokNWr8V9xcdKAUgjBTSIXp5SLHEgAIgeGBCDGjGGDt9dfd+vbk5NZf1TOZ6tVfV/TppyA6XYZRoUKTHV3JQDx2mu8shs3LtMvx8dL+YW/Cg8Hnn+ePSgTzlZj6+Xp0zkmw1PnzgGPPAJ8/TU7KU2ezFQaNzknYEgGhLCaUkoB+BrAVq31mCxu9huAp9KmYTQGcFJrbVSLactER7NEVMowzHH6NLBjh5RfCCGEkSQAkQOPAxDHj3Mn87HHGDpzQ3IyULGijFy0UoECjHq6M8jimqgoThz499+sb7NsGbtdDhqU6dbywYP8kAaU/uvjj1kR0a0bcKXfANba9OrlQf0PWEPWqhUDYBMnMgNCZZaR7rrERB4lA0LYwL0AugKIUEolpH1EKqV6KKV6pN1mHoDdAHYB+ApA7mZh21StWsyIc7tJsshWQgKr1iQDQgghjCOXtDm45RYe3Q5ATJgAnD3LC0o3yQhOe4iIYC3oiRNu3kFUFDMb/vgj869fvswRrZUrZ5ktk5DAo2RA+K/ixYFPPmHa74TP87IRZUoK8M477t1hUhLHbMbHM5vipZcMWWdSEoOiFSvmeFMhTKW1Xq21VlrrOlrremkf87TWn2utP0+7jdZa99JaV9Fa19Zax1q9biM4yzBWrjRwZLi4xuHgUTIghBDCOBKAyEHevBxa4VYA4vRpBiA6dADuusvtNaSkSP8HOwgPZ6rrypVu3kGjRkDJklmXYUyYwCaV48Zl2eDBGYCQDAj/1qkT0L49KyV2l7uXnSnHjbt+NuyqDRtYP3ToEDsndexo2BqTklhZFBJi2F0KIdzQuTN36WfMsHol/icujuWvUgIrhBDGkQCEC8LC3AxAfPEF0+0HD3b7sS9f5mNLBoT1GjVio0C3+0AEBXHqwPz5zIRIb/9+4N13mSXRvn2WdxEfz5T3YsXcXIPwCUqxUiI4GOjRA9AfjmAktHv3m587WVm2DGjRgs+71auB5s0NXWNiovR/EMIOatbkHoeUYRjP4ZDsByGEMJoEIFzgVgDiwgVg9Gjg/vuBe+5x+7H37uWuuwQgrBcaCtx7rwF9II4eBdavv/HzAwYw2jR+fLbfLg0oA0eFCsCIEUxc+N/vxfnciIsDPv0052/++WcGuypUANasMWVma1KS9H8Qwi6ioxln3LfP6pX4j7Nnga1bpf+DEEIYzf8DEMnJrKe/csXtu3ArADFlCrsFepD9AFwfwSkBCHuIiAA2bgSOHHHzDlq3ZuF8+jKMFSuAH37g87RKlSy/9fRpYNcuKb8IJD16sIKiXz/gSHg0x7IOGcIZmFkZP55Nbxs14hWJCU0aLl7khY4EIISwh+hoHqdPt3Yd/mTjRm4ASQaEEEIYy/8DEHPmsK38o49yDJ0bwsJYQu1yDOPyZeCjj4AmTYCWLd16TKeUFB6lB4Q9hIfzuGKFm3dQogSvKJ1jFZ2NJ2+9FRg4MNtv3biRdb6SARE48uQBvvoKOHUK6NdfAZ99xhKMl1+++capqQxi9e3LcZsLF7KjpQn27OFzUUowhLCHO+/kpGcpwzBOXByPkgEhhBDG8v8ARO/ePGmfMwd48EGOxcylsDCe2x8+7OI3/PgjUxcGD/Z41J0zA0I6zdtDgwZA4cIe9IEAWIaRkMAt5M8+AzZtYoPBAgWy/TZpQBmYatbkS8nUqcD8bbcD770HzJ4NzJx5/UaXLgFPP81ga8+ewC+/sGGJSWQEpxD2Ex0NxMRc37gQnnE42HqnfHmrVyKEEP7F/wMQAE/Ip01j3X3z5myskAthYTy6VIaRmgp8+CFQpw4vND2UnMxRoFkMRRBeFhzMp5DHfSAA4JtvOFqxTRtOSslBfDyHaFSo4MFjC580aBBQowYnaJ7p1pdbnS+/zNSI06fZuPR//wM++IA9IoKCTF1PUhKPkgEhhH1IGYaxHA5mP3i4jySEECKDwAhAAJxrt2ABgw9Nm7KzkItyFYCYORPYts2Q7AeAAQjp/2AvERH8L3ZrMgrAduUVKzL4cOECx2+68FxxNqCUk6HAExrKUozkZOCtoSHAl1/yCdi7N8u8liwBvv4aePNNrzxBEhMZjJOdQSHso2pV9iuYNs3qlfi+CxeAzZul/4MQQpghcAIQAAv4V6xgunKzZsxVdIHLAQitgWHDgGrVGPAwQEqKBCDsxtkHYvlyN+9AKWZBaA289hqfLzm4fJmVGlJ+EbjuvZfJXOPHA3/hHqBXL+D//o/B1Nmzgeee89pakpLYl8bkRAshRC5FRwPr1l3PUhLu+ftv9v2S/g9CCGG8wApAALyCW7OGzQBbtbpxGkEWypRhM7gcAxALF3KbeuBAQ87MtWYAQhpQ2kvduuzt51EfiJ49gaeecnlKytatjJtJA8rA9uGHDIh26wZcfncYx2QsW2ZIuVduJCZK/wch7MhZhvHLL9auw9c5G1BKBoQQQhgv8AIQAFC5MvDnn+zu1qED8O232d48OBgoW9aFAMSwYUytf/JJQ5Z5+DDTACUDwl6CgoD77vOwD0Tt2sB33wEFC7p0c2lAKQCgSBFg4kTuzn38RRFg0iSO2/SypCTp/yCEHd1+O9CwoZRheMrh4D6VnH8JIYTxAjMAATCtYdkyFvQ/+yzHZmqd5c3DwnIIQKxaBaxeDQwYAOTNa8gSnRMw5A3QfiIigN27r/8fmS0+nkMN7rzTO48n7Ouhh4DOnYGhQ4EdO7z/+OfPAwcPSgaEEHYVHQ3ExgL//GP1SnxXXByzH6TnkhBCGC9wAxAA5ynOnQs8/jjLJvr14xSLTOQYgBg2jEGNbt0MW54EIOzL2QfCoyyIXIiP52AVqbkXAPuW5s8PdO+e5UuWaZyvS5IBIYQ9de7Mo5RhuOfSJWaZSfmFEEKYI7ADEACzFf73P6BPH3Z3e/JJvvtkkG0AIi6O/R/69eNVgUGcs7ylB4T91KrF+eAe9YFwkdYswZDyC+F0yy3AqFHsqTtrlncfOzGRR8mAEMKebr0VaNxYyjDctWkTGz9LA0ohhDCHBCAAdpgcOxYYMQL48UegXTvg9OkbbhIWBhw5kmlsAhg+HChalI0FDZSczJrvYsUMvVthAKWYBbFsWbaVO4ZISgJOnpQGlOJGzzwDlCzJARje5OyuLxkQQthXdDQz53butHolvsfh4FEyIIQQwhwSgHBSCnjjDWDKFG5rh4ezC2Qa5yjOgwczfN+WLcCvvwIvv8xogYGSk6X8ws4iIoC9e4Fdu8x9HGlAKTITFAS0aQPMn+/dMozERCA0lFkYQgh7ck4ClyyI3IuL455SlSpWr0QIIfyTBCAyeuYZ5jRv2QLcey87DeJ6AOKmMowRI4ACBVjCYTAJQNibt/pAxMczSad2bXMfR/ieyEhmZjlHxnlDUhJfl/LIu4cQtlWxIk9hJACRew4HMw6lAaUQQphDTiEz064dsGQJcOwY0LQpkJCQeQAiMRH44QfgxReBUqUMX0ZKivR/sLNq1YDy5c3vAxEfD1SvzjiXEOm1bs2T5N9/995jJiZK/wchfEF0NLBxI7Btm9Ur8R2XLwMbNkj/ByGEMJMEILLSpAnHaoaEAC1a4Nbd3Oa+IQAxciTzoF991fCHP30a+PdfyYCwM2/1gZAGlCIrJUuy2dy8ed57zKQk6f8ghC/o1InvU5IF4bqtW4GLF6X/gxBCmEkCENmpWROIiQEqVkSxx9sgOs/06wGIAweAb75hyUb58oY/tIzg9A0REWwVsmWLOfd/9Cj7TEgDSpGVyEhg/Xrg0CHzH+vMGT4nJQNCCPsLCwOaN5cARG44y9kkA0IIIcwjAYicVKgArFoF1aABfkyNRvWlE/n50aOBK1fYuNIEEoDwDc4+EGaVYUgDSpGTyEgeFy40/7FkAoYQviU6Gti8mR8iZw4HUKgQSyyFEEKYQwIQrihRAli0CGuKReGpdb1YcvH558DjjwOVK5vykCkpPEoAwt5uu40XY2Y1ooyP51EyIERW6tXjRApvlGEkJvIoGRBC+IZHH2XDWMmCcI2zAaU02RVCCPPIS6yrChTAuPtm4tdizwFjxgBnzwKDBpn2cMnJQN68QNmypj2EMEh4OLB8uTmjEOPj2c28ZEnj71v4hzx5mAWxcCGTsswkGRBC+JZbbgHuu48BCDN7FfmDq1eZdSj9H4QQwlwSgMiFWyoEoxsmc/Tme+8BtWqZ9ljJybzwlCi8/UVEsGHohg3G37c0oBSuiIwETpwA1q4193ESEzmNpXRpcx9HCGGc6GhOwti0yeqV2Nv27cC5c9L/QQghzCaXt7kQFgb8e0Lh/CtvAG+/bepjJSdL+YWvMKsPxLlzPCGS8guRk/vvB4KDzR/HmZTE8gulzH0cIYRxOnbkZsbPP1u9EntzNqCUDAghhDCXBCByoVw5Hg8eNP+xUlIkAOErwsKAO+80vg/E33+zrEMyIEROihYFmjUzvw9EYqL0fxDC15Qpw0C5lGFkz+EA8ucHqle3eiVCCOHfJACRC2FhPF4bxWmSS5f4GJUqmfs4wjjh4cDKlcbW4EsDSpEbkZHAxo0c22qWpCTp/yCEL+rSBdi505xSQX8RF8eAf1CQ1SsRQgj/5nYAQilVUSm1TCm1VSm1WSnVJ5PbKKXUBKXULqXURqWUTye2eSsAsXcvdykkA8J3REQAp09fT+E0Qnw8UKyYPA+Ea5zjOOfPN+f+T5zgh2RACOF7HnmEF9YyDSNzqal8z5X+D0IIYT5PMiCuAHhVa10DQGMAvZRSNTPcpi2Aamkf3QFM8uDxLOetAERyMo9y4ek7Wrbk0cg+EM4GlFJvL1xRsyazpswqw5AJGEL4rlKlgFat2AdCyjButnMncOaM9H8QQghvcDsAobU+oLV2pP35NICtAMpnuFkHAN9rWgugmFKqnNurtViJEhyNKQEIkVHp0kDt2sb1gbhyhen0Un4hXKUUEBUFLF4MXLxo/P07AxCSASGEb+rSBdi9m70OxI2cPxMJQAghhPkM6QGhlLoNQH0A6zJ8qTyAPen+vhc3Bymc99FdKRWrlIo9cuSIEcsynFLMgjA7AJGSwmOFCuY+jjBWeDiwerUxF387dgAXLkgDSpE7kZHcxVu92vj7TkzkUQIQQvimhx/mtBwpw7hZXBwQGspMMiGEEObyOAChlCoEYAaAvlrrUxm/nMm3ZJr8p7X+UmvdQGvdoLSNh8x7IwCRnMyJG6Gh5j6OMFZEBHD+PPDXX57flzSgFO4ID+frhhnjOJOSgMKFmQkmhPA9JUoADzwg0zAy43AAdeoAISFWr0QIIfyfRwEIpVQIGHyYqrX+NZOb7AVQMd3fKwAw+fLdXN4KQEj5he9p0YJZMkb0gYiP54WkjAMTuVGwIPuRmNEHwjmCU3qSCOG7unRhMHH9eqtXYh9aMwAhDSiFEMI7PJmCoQB8DWCr1npMFjf7DcBTadMwGgM4qbU+4O5j2oEEIERWihdn/agRfSASEoC77pLdGJF7kZHA9u3AP/8Ye78yglMI39ehA99XpAzjut27gZMnpf+DEEJ4iycZEPcC6AogQimVkPYRqZTqoZTqkXabeQB2A9gF4CsAPT1brvXCwoBTp1hnbYbUVGDPHglA+KrwcCAmhqUY7tKaGRBSfiHcYcY4Tq2vZ0AIIXxXsWJA69YMQKSmWr0ae3COz5YMCCGE8A5PpmCs1lorrXUdrXW9tI95WuvPtdafp91Ga617aa2raK1ra61jjVu6NZyjOA+YlMdx+DCbGFaqZM79C3NFRACXLgFr1rh/H3v3AsePSwNK4Z6qVYFq1Ywtwzh+nEFXyYAQwvdFR3OjY13GtuEByuFgVkitWlavRAghAoMhUzACiTMAYVYZhozg9G3NmrHLuCd9IKQBpfBUZCRLgc6dM+b+ZAKGEP6jQwf2GJIyDIqL4xhtafwthBDeIQGIXJIAhMhO4cJAw4ae9YGIj2ejvzp1jFuXCCxRURzjuny5MfeXlMSjZEAI4fuKFAHatAF++UXKMJwNKKX/gxBCeI8EIHLJ7ABESgqPEoDwXRERHMV5+rR735+QwBT6QoWMXZcIHC1aAAUKGDeOUzIghPAv0dHAvn2elQv6g5QUlphJ/wchhPAeCUDkUpEiPLE3MwOiaFE+jvBN4eHA1avA6tXufb80oBSeCg0F7r+ffSC09vz+kpI45aVoUc/vSwhhvfbtgXz5pAzD2YBSMiCEEMJ7JACRS0qZO4pTRnD6vqZNgbx53esD8e+/fA5IA0rhqchIBg62bfP8vmQChhD+pXBhvkZMn86AeaByOICgICl5FEIIb5IAhBvCwthB2gwSgPB9+fMDTZq4F4BISOBRMiCEp9q25dGIaRhJSdL/QQh/Ex3NiV7uZuv5g7g4Tr/Il8/qlQghROCQAIQbGjYE/vwT6NsXuHLF2PuWAIR/iIhgKcW//+bu+5wTMCQDQniqUiXgrrs8D0BozQCEZEAI4V/atWPAPFDLMLRmAEL6PwghhHdJAMINI0YAffoA48cDrVsDx44Zc78nT/KjUiVj7k9YJzycJzcrVuTu+xISgHLlgLJlzVmXCCyRkcCqVcCpU+7fx+HDwPnzkgEhhL8pWJBBiEAtw9i3DzhyRPo/CCGEt0kAwg3BwcC4ccCUKUxdbNgQ2LjR8/uVCRj+o1Ej7izldhynNKAURoqKAi5fBpYscf8+ZAKGEP4rOppBxtwGy/2Bw8GjZEAIIYR3SQDCA888A6xcCVy4wJr/6dM9u7/kZB4lAOH78uYFmjXLXR+ICxeArVul/EIYp0kTTq7wZBxnUhKPkgEhhP+JjGQmRCCWYcTFAXnySANKIYTwNglAeKhRIyA2lm9gnTsDb70FpKa6d18SgPAvERHApk3cXXLFpk1Mg5UMCGGUkBDgwQc9G8fpzICQ1yUh/E+BAhzJOWOG8T2t7M7hAKpXZwBGCCGE90gAwgBhYcDy5cBzzwEffAA8/LB7NdcpKUBoKFCmjOFLFBYID+dx+XLXbu9sQCkBCGGkyEh2ut+wwb3vT0oCSpcGChUydFlCCJuIjgaOHnX9vcpfSANKIYSwhgQgDBIaCkyeDHzyCXcbGzcGduzI3X0kJwMVKzIlUPi+u+/mrHVX+0AkJPD2kuoujNSmDY/uTsNITJT+D0L4szZtGGD8+WerV+I9Bw7wQxpQCiGE98mlroGUAnr3BhYtYtr9PfcACxa4/v0ygtPNfESQAAAXLUlEQVS/BAcDLVq43gciPp79HyQAJYx0yy0MhrkbgEhKkqCYEP4sf36gQwfg11/ZtDYQSANKIYSwjlzqmCA8nH0hbr2V6c8jR7pWfy0BCP8TEcFMmH37sr/d1aucpCINKIUZIiOBmBjg+PHcfV9qKl+XJANCCP8WHc3Xh9w0TvZlDgc3jeQ9VwghvE8CECa57TZgzRqgUyfgjTeAJ54Azp3L+vYXLzIdUAIQ/sXZByKnMoxdu4CzZ6X/gzBHVBSDCX/8kbvvO3AAuHRJMiCE8HetWwNFigROGUZcHHDHHSx7FEII4V0SgDBRwYJ8Mx8+HPjpJ45ldE66yGjvXh4rVfLe+oT56tYFihfPOQAhDSiFmRo0AEqVyv04TucITsmAEMK/hYaygfbMmQw6+juHQ/o/CCGEVSQAYTKlgEGDgDlzgH/+4YXAihU3305GcPqnPHmAli1zTmtNSODIxJo1vbIsEWCCgthobsEClvu4yjmCUzIghPB/0dHAiRPA4sVWr8RcR44Ae/ZI/wchhLCKBCC8JCoK+OsvoGRJ4P77gYkTb+wLIQEI/xURwZ1k58VcZuLjgVq1gLx5vbYsEWAiIzlqLzbW9e9xZkBIZpYQ/u+BB4CiRf2/DMPZgFIyIIQQwhoSgPCiO+8E1q1jrWWvXkD37uz9ADAAoRRQoYK1axTGy6kPhNbXJ2AIYZYHH2RGTm6mYSQmcopG/vzmrUsIYQ958wKPPALMmnX93MQfOQMQUvIohBDWkACElxUtCsyeDQweDEyezN3xgweBlBQgLEx2wP1RzZpAmTJZByAOHGBKqJwMCTOVLAk0bpy7AISM4BS+Qin1jVLqsFJqUxZfb6mUOqmUSkj7eNvba/QFXboAp07lvmGtL4mLA6pUAYoVs3olQggRmCQAYYGgIGDYMKY5JiSwL8Sff0qas79SilkQS5dmPo5VGlAKb4mMZAnGoUOu3T4xURpQCp/xLYA2Odxmlda6XtrHUC+syee0asXGydOmWb0S8zgc0v9BCCGsJAEIC0VHM/AQHAzs2CH9H/xZRASwfz+wc+fNX0tI4LFuXe+uSQSeyEgeFyzI+bZXrrBRm2RACF+gtV4J4LjV6/B1ISFAx47M1LxwwerVGO/4cQZWpf+DEEJYRwIQFqtXD1i/Hnj6aeCJJ6xejTCLsw9EZtMw4uOZDlqkiHfXJAJPvXpAuXKujePct49BCMmAEH6kiVJqg1JqvlKqVmY3UEp1V0rFKqVijxw54u312UKXLsDp064FKn2NM+NQMiCEEMI6EoCwgdKlgW+/Bdq1s3olwixVq7LBaGZ9IBISpAGl8A6lmAXxxx/A5cvZ39Y5AUMyIISfcAC4VWtdF8AnAGZldiOt9Zda6wZa6walS5f26gLtIjycPWP8sQwjLo5HKXkUQgjrSABCCC9w9oFYtuzGPhAnTwL//CMnQ8J7IiP5vIuJyf52zrGxkgEh/IHW+pTW+kzan+cBCFFKlbJ4WbYUHAw8+ijw22/AuXNWr8ZYDgdf00qWtHolQggRuCQAIYSXRERw2sXmzdc/t2EDjxKAEN5y//28wMhpGkZSEgNn0hxX+AOl1C1KKZX253vA859j1q7Kvrp0Ac6eBebPt3olxoqLk/4PQghhNQlACOElmfWBcDaglBIM4S1FigDNm+ccgEhMBMqXl9HAwjcopX4EEAPgTqXUXqXU80qpHkqpHmk36QRgk1JqA4AJAB7TOrO5RAIAWrTg+Gh/KsM4eRLYtUsCEEIIYbVgqxcgRKC49VagcmUGIF55hZ+Lj+dJXrly1q5NBJbISGDAAE65qFgx89skJUn/B+E7tNaP5/D1TwF86qXl+DxnGcZ33zETomBBq1fkOWlAKYQQ9iAZEEJ4UXg4sGIFcPUq/x4fz+wHJgYL4R3OcZzZpVcnJkr/ByECWXQ0e0C4MjXHFzgcPEoGhBBCWEsCEEJ4UUQEcOIESy8uXQK2bJH+D8L7atRgcCGrC4tLlziGUzIghAhczZsDt9ziP2UYcXGcRlWmjNUrEUKIwCYBCCG8yNkHYtkyNqO8fFkCEML7nOM4Fy8GLl68+et79gCpqZIBIUQgCwoCOnVioPLMGatX4zmHQ7IfhBDCDiQAIYQXlSsHVK/OPhDSgFJYKTKS6dUrV978taQkHiUDQojAFh0NXLgAzJlj9Uo8c/o0sH279H8QQgg7kACEEF4WHg6sWgWsX8/GXlWrWr0iEYjCw4HQ0MynYSQm8igZEEIEtnvvBcLCfL8MY8MGQGvJgBBCCDuQAIQQXhYRwXTWH34A6tRhmqsQ3lagAIMQmQUgkpL4vKxQwevLEkLYSJ48QOfOwG+/MWvqq6+Aw4etXlXuORtQSgaEEEJYTwIQQnhZy5Y8njwp/R+EtSIjgR07gF27bvx8YiLHcwbLoGYhAt677wL9+rGEoXt3NqZs0QIYO/Z6uZbdxcVx3TLyWgghrCcBCCG8rFQpZj4AEoAQ1mrblseM4ziTkqT/gxCCihUDRo1ioHLDBuCddxhA79+frxP16wNDhwKbNrHMwY4cDsl+EEIIu5AAhBAWcE7DkAaUwkpVqwJ33HHzOM7EROn/IIS4kVIMnr/zDgMR//zDwETBgsySqF2bryevvw7ExHCSjh2cO8eR19L/QQgh7EECEEJYoHt34Pnngbp1rV6JCHRRUcDy5cDZs/z7hQvAgQOSASGEyF7lysCrrwKrVwP79wOffw5UqQKMGwc0bcoeMj17AosWceS0VTZuZDBEMiCEEMIeJAAhhAVq1gQmTwZCQqxeiQh0kZHAxYvAsmX8e3Iyj5IBIYRw1S23AC++CCxYwCaVU6cyCPHdd8CDDwJlygBduwIzZzIjwZvi4niUDAghhLAHCUAIIUQAa96cKdTOaRjOpnKSASGEcEexYsB//wtMnw4cPQrMng08/DBfYzp2ZB+kRx4Bvv8eOH7c/PU4HEDp0jLVRwgh7EJ6nAshRAALDQXuv58XB1qz/wMgGRBCCM/lzw889BA/rlwBVq5kFsSsWfwICuJkqEceYZCifHnj1xAXx+wHpYy/byGEELknGRBCCBHgIiNZerF1KzMgQkKAsDCrVyWE8CfBwUBEBPDJJ0BKCvDXX2xYuXcv0Ls3MxQaNwY++ojjgY1w4QKwebP0fxBCCDuRAIQQQgQ45zjOefMYgLj1ViCPvDsIIUyiFNCwITB8OLBtG6dUDBsGXL0KDBwI3HkncNddwFtvsYTC3fGef//NzAvp/yCEEPYhp5hCCBHgKlbkCL3ff2cJhvR/EEJ4U40awODBwPr1zMYaP559G4YPZ/bC7bcDffuyhOPqVdfv1+HgUQIQQghhHxKAEEIIgagojtPbtk36PwghrFOpEvDKK5zMc+gQ8M03QJ06HPN5331AuXJAt24MmF68mP19xcUBxYvLa5oQQtiJRwEIpdQ3SqnDSqlNWXy9pVLqpFIqIe3jbU8eTwghhDkiI5mqfOqUnKwLIeyhVCng2WeB334DjhwBpk1j09xffgHatePXH3sM+Pln4PTpm7/f4ZAGlEIIYTeeZkB8C6BNDrdZpbWul/Yx1MPHE0IIYYImTYCiRflnKcEQQthN4cJA587ADz8Ahw+zZ83jjzNT4rHHGIyIigImT+bXL11iDwhpQCmEEPbiUQBCa70SgBemOAshhDBTcDDQujX/LBkQQgg7Cw1l89wvvwT27wdWreIkjS1bgBdeYJlG48YMQkj/ByGEsBdv9IBoopTaoJSar5SqldWNlFLdlVKxSqnYI0eOeGFZQggh0nviCaBYMaB6datXIoQQrgkKApo1A0aPBnbvBuLj/7+9ew+ysq7jOP7+BgR4ocBNRVZhVJxpSyJcLbIc1MwgbzmMYmOTjTMN6piNNZMlXc0aNFMUG8esvJS3IksZMY20vIC6KnI1VEIhEC+ouAty89sfv9+ZfVh39+zZPZfnd/bzmjmzZ5/nPM/58N3feS6/8zw/YMaMcEvZ0KFw5JG1TigiIlnmvf2/jQorMBsDzHX3j3cybxjwnru3mtkUYJa7jy22zubmZm9paelTLhERKZ277pfOMzN7yt2ba52jP9CxSPp27gwdFCIiUl59OR6p6BUQ7r7J3Vvj83uBQWbWUMn3FBGR3lPng4jUC3U+iIjkT0U7IMxsX7NwOGtmR8T3e6OS7ykiIiIiIiIi+TOwLwub2W3AJKDBzNYCPwIGAbj7dcBU4Bwz2wFsAaZ5X+/5EBEREREREZHk9KkDwt3PKDJ/NjC7L+8hIiIiIiIiIumrxv+CISIiIiIiIiL9nDogRERERERERKTi1AEhIiIiIiIiIhWnDggRERERERERqTh1QIiIiIiIiIhIxakDQkREREREREQqTh0QIiIiIiIiIlJx6oAQERERERERkYozd691hvcxs9eAl8q4ygbg9TKurz9TLctHtSwf1bJ8VMvyqUQtR7v7R8q8TulEBY5FIN3Pl3JXX6rZlbu6lLu6lLtdr49HctkBUW5m1uLuzbXOUQ9Uy/JRLctHtSwf1bJ8VEvpKNU2odzVl2p25a4u5a4u5S4P3YIhIiIiIiIiIhWnDggRERERERERqbj+0gFxfa0D1BHVsnxUy/JRLctHtSwf1VI6SrVNKHf1pZpduatLuatLucugX4wBISIiIiIiIiK11V+ugBARERERERGRGlIHhIiIiIiIiIhUXE06IMxsfzN70MxWmNkyM7sgTh9hZg+Y2fPx5/A4fa/4+lYzm93FOu82s6XdvOdhZrbEzF4ws6vNzOL0A+K6nzGzxWY2pYvlB5vZHXH5x81sTGbefWb2lpnN7X1VeqcOa7nTzBbFx929r0zp6rCWM81saXyc3vvKlC7RWh5lZk+b2Q4zm5qZPtrMnoptcpmZTe9LbUpVZ7U8OvP5XmRm75rZKX2pTykSreWFZrY8vma+mY3OzKvZvqdeJNwmVsYMbfH5BWY23syejNO3xnWkkHt03B68E3OvSCF3nDfCzP5pYVv3ckK5d2bq/WpCuQ81szfMbFucPy5nuZfH9rvRwrZjmZldE9v3kljz9+LvqdT86jh9W2JtfJbld1s43cyei+1hc1xPIfe5cdo2C8fvecu9JLbfR8ysKTOvtOMRd6/6AxgJTIjP9wRWAk3AZcBFcfpFwMz4fHfgs8B0YHYn6zsVuBVY2s17PgFMBAyYB0yO068HzonPm4DVXSx/LnBdfD4NuCMz71jgRGCuatnnWrbWok3WWy2BLwEPAANjzhZgmGrZbS3HAOOAm4GpmekfBAbH53sAq4H9VMvSa9nhNSOAjcBuqmW3tTy6UCPgHHKy76mXR8Jt4kBgQmwTc2LuycBvYt79gE3AVQnk/gRwRcy9B/AmcG0CuQvt5NGY4bFE2kkTsI302ncT8DJwQ3zdD4ErcpZ7N8I25efAHbx/m/ITwn7vB4nU/CvAWuB7wADgJeDWBHJPJxyrfT/mXAtcmaPcw2I7mQCcRDheXxnX+VZsJ8OBN8jXNnxY5vlJwH2Z30s6HqnJFRDuvt7dn47P3wFWAKOAk4Gb4stuAk6Jr2lz90eAdzuuy8z2AC4EftbV+5nZSELRFnio0s2FdQNOaAgAHwLWdbGabLY/A8cWeo7cfT7wTpF/dkXUWy1rqc5q2QT8y913uHsb8Czwxe4rUD4p1tLdV7v7YuC9DtO3ufvW+OtgqnzlWD3VsoOpwDx339zNa8oq0Vo+mKnRQqAxM69m+556kXCbWBVzLwT2jbl3AEcBN7n7OmAN4SAx77n3Bk4g1Hkw0AYcn0DuUcBpwGvA/cBzpNFORhG+nEitfU8EGggn7xA6207IWe7N7r4e+BPQ2Mk2pZVw4ncDadS8IT5uJ3w23yL8HfKeewLhhP338Rj438DpOcq9KbPv2Z2w7V5BOIGH8MXim8C9hG1MbnJnft09LleYV9LxSM3HgLBwyfgngceBfeIHl/hz7x6s4hJCz3l3B7GjCL1fBWvjNIAfA2ea2VrCH/r8btaxJmbbAbwN7NWDfFVTJ7UcYmYtZrbQqnhpdkd1UMtngclmtpuZNRB6iffvQe6yS6iWXbJwmfZiQq1nxoP7qquHWmZMA27rw/J9kmgtzyYcvEoFJNwmFnTMbWZHEI7xhieSe1/g74Rt7C/o2fFVrXM/QegQLCy3mXTaiQH3mNlC4FOJ5N4MDAKuNbNnCCdcec49r+M2hXCV0m0JbVNujNOeAdYDc4EPJ5D7XsK27+14DPzpvOU2s/PM7EXClTEzY+42whW36+PLVuY89zd7kK1TNe2AiD02c4BvdehV6eny44GD3f2uYi/tZFqh1+YM4EZ3bwSmALeYWWd16W4dNVdHtTzA3ZsJl31dZWYHFU9fXvVQS3e/n7AReYxwkreA0MNaVYnVskvuvsbdxwEHA18zs31KWb4c6qWWMctI4FDCCUfVpVhLMzsTaAYuLzWvFJdwmzgCOCabO36+bgG+nlBuz25ju3i/XOUGvgpsd/c1RTLkKnes96bssRZFzgfykBvYTrhy4zvA4YRL7z+Y09zNwK/ZdZtilLDfy0nN9ybcetFIOFk9hvA3yHVud/8rob1kj4G7D1Pl3O5+rbsfRLiVaA6h3tuK5cxR7u8CM0rNW1CzDggzG0Qo+B/d/S9x8oa44yzsQF8tspqJwGFmthp4BDjEzB4yswHWPsjZTwm9PI2Z5Rppv7zkbOBOAHdfAAwBGszs0sI64uvWEr9BNrOBhEtUNvbyn19W9VTLwjfL7r4KeIjQI1g1dVbLS919vLsfR9jwPN+LkvRagrUsKrbPZcDnerpMOdRhLU8D7nL37T18fdmkWEsz+zxwMXCSt98OJGWSeJtoA/6Qyf0a4QRnBuF+7VRybzCzkXEbuyrOz3vuicBAM1sD/JLQcdLdbWd5yQ3wSqz3KsK3860J5F4LbAG2xCs+H6T4CVutcp9KOPHNblPeJdwzvz2hbcqXCVfW7unurcDDhL9B3nNDGC9kcjwGHkrxc7aqHyPFfc80wjhPhTa+tbDvAQ4h3PaSq9zR7bTfwlE678FAEeV+EE6GbiYOrJGZfjm7DopzWYf5Z9HJwBtx3hi6H3jjScIlOIWBN6bE6fOAs+Lzj8Y/hnWy/HnsOtjfnR3mT6I2g1DWTS0Jl0sVBvtrIJwwN6mWvarlAGCv+HwcsBQYqFp2XcvMem5k10EoG4GhmTa6EjhUtSy9lpnpC4Gjq1XDlGtJ6IR9ERjbxfonoUEo+2ubuCubm/Bt8EvAPYnlbgSujHmHEzpRfpf33Nl2EvM9lki9hwO/irkbgNeB3yaQewDhpP2S+HsL8EAOc4+l823KOuD6xD6bp9M+mOMg4AVgTgK5BwDXxDqPA14BLs9R7rG073v+BrTE6SPYdRDKjcDVecqdeX5iIXdm2iR6eDxS9AWVeBBG8XRgMbAoPqYQ7vmbTzjxnA+MyCyzOv4hWgm9OE0d1lms6M2Ek7AXgdmFwhIG63uUcM/8IuALXSw/hDCozAuE+/4OzMx7mLDD3BKzHa9all5L4DPAkrj8EuBstcte13IIsDw+FgLjVcuitTw8vm8bYeThZXH6cfHf8Wz8+Q3Vsne1zLz3/4APVLOOCdfyH8CGTN67M/Nqtu+pl0fCbWJjzL2F8A3lIsLJ8HbCQGBb48+jEsh9MeHKskLuFYnUO9tOXiGMX5FC7m8Tjg0K9V6eSO4phCsLCrnXEcY8yVPuDYRthmdyLyKcNK4nvW3KCYQvEtpizV9OJPfJwH9i7i2Eq3zylHsW8N+Yu5UwgG2hjZ9PGMthG/n7bM4ibKsXEa5A+lhmXknHI4U3FhERERERERGpmJr/LxgiIiIiIiIiUv/UASEiIiIiIiIiFacOCBERERERERGpOHVAiIiIiIiIiEjFqQNCRERERERERCpOHRAiIiIiIiIiUnHqgBARERERERGRivs/zJObH0YRIJQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1296x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import numpy as np\n",
    "\n",
    "import tensorflow\n",
    "tf = tensorflow.compat.v1\n",
    "# 关闭动态图执行\n",
    "tf.disable_eager_execution()\n",
    "\n",
    "tf.reset_default_graph()\n",
    "\n",
    "def generate_data():\n",
    "    dateparse = lambda dates: pd.datetime.strptime(dates, '%Y%m%d')\n",
    "    user_balance = pd.read_csv('user_balance_table.csv', parse_dates=['report_date'],\n",
    "                               date_parser=dateparse)\n",
    "\n",
    "    mfd_day_share_interest = pd.read_csv('mfd_day_share_interest.csv', parse_dates=['mfd_date'],\n",
    "                                         date_parser=dateparse)\n",
    "    mfd_day_share_interest.index = mfd_day_share_interest['mfd_date']\n",
    "\n",
    "    mfd_bank_shibor = pd.read_csv('mfd_bank_shibor.csv', parse_dates=['mfd_date'], date_parser=dateparse)\n",
    "    mfd_bank_shibor.index = mfd_bank_shibor['mfd_date']\n",
    "\n",
    "    user_balance = user_balance.groupby(['report_date'])['share_amt', 'total_purchase_amt'].sum()\n",
    "    user_balance.reset_index(inplace=True)\n",
    "    user_balance.index = user_balance['report_date']\n",
    "\n",
    "    user_balance = user_balance['2014-03-01':'2014-08-31']\n",
    "    mfd_day_share_interest = mfd_day_share_interest['2014-03-01':'2014-08-31']\n",
    "    mfd_bank_shibor = mfd_bank_shibor['2014-03-01':'2014-08-31']\n",
    "\n",
    "    data = {'share_amt': user_balance['share_amt'],\n",
    "            'total_purchase_amt': user_balance['total_purchase_amt'],\n",
    "            'mfd_daily_yield': mfd_day_share_interest['mfd_daily_yield'],\n",
    "            'mfd_7daily_yield': mfd_day_share_interest['mfd_7daily_yield'],\n",
    "            'Interest_O_N': mfd_bank_shibor['Interest_O_N'],\n",
    "            'Interest_1_W': mfd_bank_shibor['Interest_1_W'],\n",
    "            'Interest_1_M': mfd_bank_shibor['Interest_1_M']}\n",
    "\n",
    "    df = pd.DataFrame(data=data, index=user_balance.index)\n",
    "\n",
    "    df['Interest_O_N'].fillna(df['Interest_O_N'].median(), inplace=True)\n",
    "    df['Interest_1_W'].fillna(df['Interest_1_W'].median(), inplace=True)\n",
    "    df['Interest_1_M'].fillna(df['Interest_1_M'].median(), inplace=True)\n",
    "\n",
    "    df.to_csv(path_or_buf='multi_purchase_seq.csv')\n",
    "\n",
    "\n",
    "# 数据集归一化\n",
    "def get_normal_data(purchase_seq):\n",
    "    scaler_x = MinMaxScaler(feature_range=(0, 1))\n",
    "    scaler_y = MinMaxScaler(feature_range=(0, 1))\n",
    "    scaled_x_data = scaler_x.fit_transform(purchase_seq[ ['Interest_1_M', 'Interest_1_W', 'Interest_O_N', 'mfd_7daily_yield', 'mfd_daily_yield', 'share_amt']])\n",
    "    scaled_y_data = scaler_y.fit_transform(purchase_seq[['total_purchase_amt']])\n",
    "    return scaled_x_data, scaled_y_data, scaler_y\n",
    "\n",
    "\n",
    "# 构造训练集\n",
    "def get_train_data(scaled_x_data, scaled_y_data, divide_train_valid_index, time_step):\n",
    "    train_x, train_y = [], []\n",
    "    normalized_train_feature = scaled_x_data[0: -divide_train_valid_index]\n",
    "    normalized_train_label = scaled_y_data[0: -divide_train_valid_index]\n",
    "    for i in range(len(normalized_train_feature) - time_step + 1):\n",
    "        train_x.append(normalized_train_feature[i:i + time_step].tolist())\n",
    "        train_y.append(normalized_train_label[i:i + time_step].tolist())\n",
    "    return train_x, train_y\n",
    "\n",
    "\n",
    "# 构造拟合训练集\n",
    "def get_train_fit_data(scaled_x_data, scaled_y_data, divide_train_valid_index, time_step):\n",
    "    train_fit_x, train_fit_y = [], []\n",
    "    normalized_train_feature = scaled_x_data[0: -divide_train_valid_index]\n",
    "    normalized_train_label = scaled_y_data[0: -divide_train_valid_index]\n",
    "    train_fit_remain = len(normalized_train_label) % time_step\n",
    "    train_fit_num = int((len(normalized_train_label) - train_fit_remain) / time_step)\n",
    "    temp = []\n",
    "    for i in range(train_fit_num):\n",
    "        train_fit_x.append(normalized_train_feature[i * time_step:(i + 1) * time_step].tolist())\n",
    "        temp.extend(normalized_train_label[i * time_step:(i + 1) * time_step].tolist())\n",
    "    if train_fit_remain > 0:\n",
    "        train_fit_x.append(normalized_train_feature[-time_step:].tolist())\n",
    "        temp.extend(normalized_train_label[-train_fit_remain:].tolist())\n",
    "    for i in temp:\n",
    "        train_fit_y.append(i[0])\n",
    "    return train_fit_x, train_fit_y, train_fit_remain\n",
    "\n",
    "\n",
    "# 构造验证集\n",
    "def get_valid_data(scaled_x_data, scaled_y_data, divide_train_valid_index, divide_valid_test_index, time_step):\n",
    "    valid_x, valid_y = [], []\n",
    "    normalized_valid_feature = scaled_x_data[-divide_train_valid_index: -divide_valid_test_index]\n",
    "    normalized_valid_label = scaled_y_data[-divide_train_valid_index: -divide_valid_test_index]\n",
    "    valid_remain = len(normalized_valid_label) % time_step\n",
    "    valid_num = int((len(normalized_valid_label) - valid_remain) / time_step)\n",
    "    temp = []\n",
    "    for i in range(valid_num):\n",
    "        valid_x.append(normalized_valid_feature[i * time_step:(i + 1) * time_step].tolist())\n",
    "        temp.extend(normalized_valid_label[i * time_step:(i + 1) * time_step].tolist())\n",
    "    if valid_remain > 0:\n",
    "        valid_x.append(normalized_valid_feature[-time_step:].tolist())\n",
    "        temp.extend(normalized_valid_label[-valid_remain:].tolist())\n",
    "    for i in temp:\n",
    "        valid_y.append(i[0])\n",
    "    return valid_x, valid_y, valid_remain\n",
    "\n",
    "\n",
    "# 构造测试集\n",
    "def get_test_data(scaled_x_data, scaled_y_data, divide_valid_test_index, time_step):\n",
    "    test_x, test_y = [], []\n",
    "    normalized_test_feature = scaled_x_data[-divide_valid_test_index:]\n",
    "    normalized_test_label = scaled_y_data[-divide_valid_test_index:]\n",
    "    test_remain = len(normalized_test_label) % time_step\n",
    "    test_num = int((len(normalized_test_label) - test_remain) / time_step)\n",
    "    temp = []\n",
    "    for i in range(test_num):\n",
    "        test_x.append(normalized_test_feature[i * time_step:(i + 1) * time_step].tolist())\n",
    "        temp.extend(normalized_test_label[i * time_step:(i + 1) * time_step].tolist())\n",
    "    if test_remain > 0:\n",
    "        test_x.append(scaled_x_data[-time_step:].tolist())\n",
    "        temp.extend(normalized_test_label[-test_remain:].tolist())\n",
    "    for i in temp:\n",
    "        test_y.append(i[0])\n",
    "    return test_x, test_y, test_remain\n",
    "\n",
    "generate_data()\n",
    "\n",
    "# 模型参数\n",
    "lr = 1e-3  # 学习率\n",
    "batch_size = 10  # minibatch大小\n",
    "rnn_unit = 40  # LSTM 隐藏层神经元数量\n",
    "input_size = 6  # 单元的输入数量\n",
    "output_size = 1  # 单元的输出数量\n",
    "time_step = 15  # 时间长度\n",
    "epochs = 1000  # 训练次数\n",
    "gradient_threshold = 5  # 梯度裁剪阈值\n",
    "stop_loss = np.float32(0.025)  # 训练停止条件。当训练误差 + 验证误差小于阈值时，停止训练\n",
    "train_keep_prob = [1.0, 0.5, 1.0]  # 训练时 dropout 神经元保留比率\n",
    "\n",
    "# 数据切分参数\n",
    "divide_train_valid_index = 31\n",
    "divide_valid_test_index = 10\n",
    "\n",
    "# 数据准备\n",
    "dateparse = lambda dates: pd.datetime.strptime(dates, '%Y-%m-%d')\n",
    "purchase_seq = pd.read_csv('multi_purchase_seq.csv', parse_dates=['report_date'], index_col='report_date', date_parser=dateparse)\n",
    "\n",
    "scaled_x_data, scaled_y_data, scaler_y = get_normal_data(purchase_seq)\n",
    "train_x, train_y = get_train_data(scaled_x_data, scaled_y_data, divide_train_valid_index, time_step)\n",
    "train_fit_x, train_fit_y, train_fit_remain = get_train_fit_data(scaled_x_data, scaled_y_data, divide_train_valid_index, time_step)\n",
    "valid_x, valid_y, valid_remain = get_valid_data(scaled_x_data, scaled_y_data, divide_train_valid_index, divide_valid_test_index, time_step)\n",
    "test_x, test_y, test_remain = get_test_data(scaled_x_data, scaled_y_data, divide_valid_test_index, time_step)\n",
    "\n",
    "\n",
    "def lstm(X, keep_prob):\n",
    "    batch_size = tf.shape(X)[0]  # minibatch 大小\n",
    "\n",
    "    # 输入到 LSTM 输入的转换，一层全连接的网络，其中权重初始化采用截断的高斯分布，激活函数采用tanh\n",
    "    weights = tf.Variable(tf.truncated_normal(shape=[input_size, rnn_unit]))\n",
    "    biases = tf.Variable(tf.constant(0.1, shape=[rnn_unit, ]))\n",
    "    input = tf.reshape(X, [-1, input_size])\n",
    "\n",
    "    input_layer = tf.nn.tanh(tf.matmul(input, weights) + biases)\n",
    "    input_rnn = tf.nn.dropout(input_layer, keep_prob[0])\n",
    "    input_rnn = tf.reshape(input_rnn, [-1, time_step, rnn_unit])\n",
    "\n",
    "    # 两层 LSTM 网络，激活函数默认采用 tanh，当网络层数较深时，建议使用 relu\n",
    "    initializer = tf.truncated_normal_initializer()\n",
    "    cell_1 = tf.nn.rnn_cell.LSTMCell(forget_bias=1.0, num_units=rnn_unit, use_peepholes=True, num_proj=None, initializer=initializer, name='lstm_cell_1')\n",
    "    cell_1_drop = tf.nn.rnn_cell.DropoutWrapper(cell=cell_1, output_keep_prob=keep_prob[1])\n",
    "\n",
    "    cell_2 = tf.nn.rnn_cell.LSTMCell(forget_bias=1.0, num_units=rnn_unit, use_peepholes=True, num_proj=output_size, initializer=initializer, name='lstm_cell_2')\n",
    "    cell_2_drop = tf.nn.rnn_cell.DropoutWrapper(cell=cell_2, output_keep_prob=keep_prob[2])\n",
    "\n",
    "    mutilstm_cell = tf.nn.rnn_cell.MultiRNNCell(cells=[cell_1_drop, cell_2_drop], state_is_tuple=True)\n",
    "    init_state = mutilstm_cell.zero_state(batch_size, dtype=tf.float32)\n",
    "\n",
    "    with tf.variable_scope('lstm', reuse=tf.AUTO_REUSE):\n",
    "        output, state = tf.nn.dynamic_rnn(cell=mutilstm_cell, inputs=input_rnn, initial_state=init_state, dtype=tf.float32)\n",
    "\n",
    "    return output, state\n",
    "\n",
    "# 获取拟合数据，这里用于拟合，关闭 dropout\n",
    "def get_fit_seq(x, remain, sess, output, X, keep_prob, scaler, inverse):\n",
    "    fit_seq = []\n",
    "    if inverse:\n",
    "        # 前面对数据进行了归一化，这里反归一化还原数据\n",
    "        temp = []\n",
    "        for i in range(len(x)):\n",
    "            next_seq = sess.run(output, feed_dict={X: [x[i]], keep_prob: [1.0, 1.0, 1.0]})\n",
    "            if i == len(x) - 1:\n",
    "                temp.extend(scaler.inverse_transform(next_seq[0].reshape(-1, 1))[-remain:])\n",
    "            else:\n",
    "                temp.extend(scaler.inverse_transform(next_seq[0].reshape(-1, 1)))\n",
    "        for i in temp:\n",
    "            fit_seq.append(i[0])\n",
    "    else:\n",
    "        for i in range(len(x)):\n",
    "            next_seq = sess.run(output,\n",
    "                                feed_dict={X: [x[i]], keep_prob: [1.0, 1.0, 1.0]})\n",
    "            if i == len(x) - 1:\n",
    "                fit_seq.extend(next_seq[0].reshape(1, -1).tolist()[0][-remain:])\n",
    "            else:\n",
    "                fit_seq.extend(next_seq[0].reshape(1, -1).tolist()[0])\n",
    "\n",
    "    return fit_seq\n",
    "\n",
    "\n",
    "def train_lstm():\n",
    "    X = tf.placeholder(tf.float32, [None, time_step, input_size])\n",
    "    Y = tf.placeholder(tf.float32, [None, time_step, output_size])\n",
    "\n",
    "    keep_prob = tf.placeholder(tf.float32, [None])\n",
    "    output, state = lstm(X, keep_prob)\n",
    "    loss = tf.losses.mean_squared_error(tf.reshape(output, [-1]), tf.reshape(Y, [-1]))\n",
    "\n",
    "    # 梯度优化与裁剪\n",
    "    optimizer = tf.train.AdamOptimizer(learning_rate=lr)\n",
    "    grads, variables = zip(*optimizer.compute_gradients(loss))\n",
    "    grads, global_norm = tf.clip_by_global_norm(grads, gradient_threshold)\n",
    "    train_op = optimizer.apply_gradients(zip(grads, variables))\n",
    "\n",
    "    X_train_fit = tf.placeholder(tf.float32, [None])\n",
    "    Y_train_fit = tf.placeholder(tf.float32, [None])\n",
    "    train_fit_loss = tf.losses.mean_squared_error(tf.reshape(X_train_fit, [-1]), tf.reshape(Y_train_fit, [-1]))\n",
    "\n",
    "    X_valid = tf.placeholder(tf.float32, [None])\n",
    "    Y_valid = tf.placeholder(tf.float32, [None])\n",
    "    valid_fit_loss = tf.losses.mean_squared_error(tf.reshape(X_valid, [-1]), tf.reshape(Y_valid, [-1]))\n",
    "\n",
    "    with tf.Session() as sess:\n",
    "        sess.run(tf.global_variables_initializer())\n",
    "        fit_loss_seq = []\n",
    "        valid_loss_seq = []\n",
    "\n",
    "        for epoch in range(epochs):\n",
    "            for index in range(len(train_x) - batch_size + 1):\n",
    "               sess.run(train_op, feed_dict={X: train_x[index: index + batch_size], Y: train_y[index: index + batch_size], keep_prob: train_keep_prob})\n",
    "\n",
    "            # 拟合训练集和验证集\n",
    "            train_fit_seq = get_fit_seq(train_fit_x, train_fit_remain, sess, output, X, keep_prob, scaler_y, False)\n",
    "            train_loss = sess.run(train_fit_loss, {X_train_fit: train_fit_seq, Y_train_fit: train_fit_y})\n",
    "            fit_loss_seq.append(train_loss)\n",
    "\n",
    "            valid_seq = get_fit_seq(valid_x, valid_remain, sess, output, X, keep_prob, scaler_y, False)\n",
    "            valid_loss = sess.run(valid_fit_loss, {X_valid: valid_seq, Y_valid: valid_y})\n",
    "            valid_loss_seq.append(valid_loss)\n",
    "\n",
    "            print('epoch:', epoch + 1, 'fit loss:', train_loss, 'valid loss:', valid_loss)\n",
    "\n",
    "            # 提前终止条件。\n",
    "            # 常见的方法是验证集达到最小值，再往后训练 n 步，loss 不再减小，实际测试这里使用的效果不好。\n",
    "            # 这里选择 stop_loss 是经过多次尝试得到的阈值。\n",
    "            if train_loss + valid_loss <= stop_loss:\n",
    "                train_fit_seq = get_fit_seq(train_fit_x, train_fit_remain, sess, output, X, keep_prob, scaler_y, True)\n",
    "                valid_fit_seq = get_fit_seq(valid_x, valid_remain, sess, output, X, keep_prob, scaler_y, True)\n",
    "                test_fit_seq = get_fit_seq(test_x, test_remain, sess, output, X, keep_prob, scaler_y, True)\n",
    "                print('best epoch: ', epoch + 1)\n",
    "                break\n",
    "\n",
    "    return fit_loss_seq, valid_loss_seq, train_fit_seq, valid_fit_seq, test_fit_seq\n",
    "\n",
    "\n",
    "fit_loss_seq, valid_loss_seq, train_fit_seq, valid_fit_seq, test_fit_seq = train_lstm()\n",
    "\n",
    "\n",
    "# 切分训练集、验证集、测试集\n",
    "purchase_seq_train = purchase_seq[0:-divide_train_valid_index]\n",
    "purchase_seq_valid = purchase_seq[-divide_train_valid_index:-divide_valid_test_index]\n",
    "purchase_seq_test = purchase_seq[-divide_valid_test_index:]\n",
    "\n",
    "plt.figure(figsize=(18, 12))\n",
    "\n",
    "plt.subplot(221)\n",
    "plt.title('loss')\n",
    "plt.plot(fit_loss_seq, label='fit_loss', color='blue')\n",
    "plt.plot(valid_loss_seq, label='valid_loss', color='red')\n",
    "plt.legend(loc='best')\n",
    "\n",
    "plt.subplot(222)\n",
    "plt.title('train')\n",
    "seq_train_fit = pd.DataFrame(columns=['total_purchase_amt'], data=train_fit_seq, index=purchase_seq_train.index)\n",
    "plt.plot(purchase_seq_train['total_purchase_amt'], label='value', color='blue')\n",
    "plt.plot(seq_train_fit['total_purchase_amt'], label='fit_value', color='red')\n",
    "plt.legend(loc='best')\n",
    "\n",
    "plt.subplot(223)\n",
    "plt.title('valid')\n",
    "seq_valid_fit = pd.DataFrame(columns=['total_purchase_amt'], data=valid_fit_seq, index=purchase_seq_valid.index)\n",
    "plt.plot(purchase_seq_valid['total_purchase_amt'], label='value', color='blue')\n",
    "plt.plot(seq_valid_fit['total_purchase_amt'], label='fit_value', color='red')\n",
    "plt.legend(loc='best')\n",
    "\n",
    "plt.subplot(224)\n",
    "plt.title('test')\n",
    "seq_test_fit = pd.DataFrame(columns=['total_purchase_amt'], data=test_fit_seq, index=purchase_seq_test.index)\n",
    "plt.plot(purchase_seq_test['total_purchase_amt'], label='value', color='blue')\n",
    "plt.plot(seq_test_fit['total_purchase_amt'], label='fit_value', color='red')\n",
    "plt.legend(loc='best')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}